Post on 15-Mar-2023
9
School of Engineering & Technology
Syllabi and Course Structure
M. Tech. in Computer Science and
Engineering
Academic Programs
April, 2019
10
School of Engineering & Technology
M.Tech. in Computer Science & Engineering
Course Structure
First Semester
First Semester
Sub Code Sub Name L T P C
MCO 056A Advanced Data Structure and Algorithms design 4 0 0 4
MCO 007A Advance Data Communication Network 4 0 0 4
MCO 003A Advanced Operating Systems 4 0 0 4
MCO 014A Advance Topics in data mining
and warehousing
Elective I
4 0 0 4
MCO 021A Digital image processing
MCO 016A Information System security
MCO 011A Cloud Computing
MCO 070A Advanced Data Structure and Algorithm Lab 0 0 2 2
MCO 036A Advance Technology lab 0 0 2 2
MCO 010A Seminar 0 0 2 2
TOTAL 16 0 06 22
11
School of Engineering & Technology
M.Tech. inComputer Science & Engineering
Second Semester
SECOND SEMESTER
Sub Code Sub Name L T P C
MCO 093A Advance database management system 4 0 0 4
MCO 094A Distributed Algorithms 4 0 0 4
HS0001 Research Methodology & Technical
communication
3 0 0 3
MCO 095A Soft Computing Elective-II 4 0 0 4
MCO 075A Internet of things
MCO 096A Cyber Security and Laws
Quantitative Techniques & Computer
Applications Lab
0 0 1 1
MCO 097A Advance database management system lab 0 0 2 2
MCO 036A Advance Technology Lab 0 0 2 2
MCO 019A Project 0 0 2 2
TOTAL 15 0 07 22
12
School of Engineering & Technology
M.Tech. inComputer Science & Engineering
Third Semester
THIRD SEMESTER
Sub Code Sub Name L T P C
MCO 98A Advance Compiler Design 4 0 0 4
MCO 083A Big Data Security 4 0 0 4
MCO 099A Human Interface System
Design
Elective III
4 0 0 4
MCO 087A Machine Learning
MCO 024A Grid computing
MCO 100A Fuzzy Logic
Elective IV
4 0 0 4
MCO 026A Biometric Security
MCO 101A Neural Networks
Programming Techniques
MCO 006A Client server programming
MCO 029A Dissertation-I 0 0 0 12
TOTAL 16 0 0 28
Fourth Semester
FOURTH SEMESTER
MCO 030A Dissertation-II 0 0 0 28
TOTAL 0 0 0 28
13
MCO 056A Advanced Data Structure and Algorithms design 4-0-0
Course Objective
• To understand the various algorithm design technique.
• To learn analysis techniques to analyze the algorithms.
• To understand the advanced data structures, intrinsic complexity analysis, problem
settings
UNIT 1
Advanced Data Structure: Graph, B-tree, binomial heaps and, Fibbonacci heap,
Red black tree
UNIT 2
Graph Algorithms: Single source shortest paths-Belman-Ford algorithm,
Dijkistra algorithm, all pairs shortest path and matrix multiplication, Floyad-
Warshallalhm, Johnson algorithm for parse graph, maximum flow-Ford-
Fulkusonmethod and maximum bipartite matching.
UNIT 3
Number Theoretic Algorithm: GCD, modular arithmetic, solving modular
linear equation and Chinese remainder theorem.
Amortized Analysis, Data Structures for Disjoint Sets
UNIT 4
NP Completeness: Polynomial time, polynomial time verification, NP
completeness andreducibility, Cook’s theorem, NP complete problems-clique
problem, vertex cover problem,approximation algorithms-vertex cover problem,
set covering problem, traveling salesmanproblem.
UNIT 5
Probabilistic Algorithms: Numerical probabilistic algorithm, Monte-Carlo
algorithm and Las-Vegas algorithm.
Sorting network,
At the end of the course, the student should be able to:
• Understand the various algorithm design technique.
• Learn analysis techniques to analyze the algorithms.
• Understand the advanced data structures, intrinsic complexity analysis,problem settings
Text Books:
1. Cormen T.H., Leiserson C.E., Rivest R.L., Introduction to Algorithms , Prentice Hall of
India
Refrence Books:
1. Brassad G. &Bratley P., Fundamentals of Algorithmics , Prentice Hall of India
14
MCO 007A Advanced Data Communication Network 4-0-0
Course Objective
1. To provide a good conceptual understanding of advance computer networking
2. To understand various models and their functions
3. To have an advance understanding of performance evaluation
4. To understand network economics
Module 1:
The Motivation for Internetworking; Need for Speed and Quality of Service;
History of Networking and Internet; TCP/IP and ATM Networks; Internet
Services; TCP Services; TCP format and connection management;
Encapsulation in IP; UDP Services, Format and Encapsulation in IP; IP Services;
Header format and addressing; Fragmentation and reassembly; classless and
subnet address extensions; sub netting and super netting; CIDR; IPv6;
Module 2:
Congestion Control and Quality of Service: Data traffic; Network performance;
Effects of Congestion; Congestion Control; Congestion control in TCP and
Frame Relay; Link-Level Flow and Error Control; TCP flow control; Quality of
Service: Flow Characteristics, Flow Classes; Techniques to improve QoS;
Traffic Engineering; Integrated Services;
Module 3:
High Speed Networks: Packet Switching Networks; Frame Relay Networks;
Asynchronous Transfer Mode (ATM); ATM protocol Architecture; ATM logical
connections; ATM cells; ATM Service categories; ATM Adaptation Layer;
Optical Networks: SONET networks; SONET architecture;
Wireless WANs: Cellular Telephony; Generations; Cellular Technologies in
different generations; Satellite Networks;
Module 4:
Internet Routing: Interior and Exterior gateway Routing Protocols; Routers and
core routers; RIP; OSPF; BGP; IDRP; Multicasting; IGMP; MOSPF; Routing in
Ad Hoc Networks; Routing in ATM: Private Network-Network Interface;
Module 5:
Error and Control Messages: ICMP; Error reporting vs Error Correction; ICMP
message format and Delivery; Types of messages;
Address Resolution (ARP); BOOTP; DHCP; Remote Logging; File Transfer and
Access; Network Management and SNMP; Comparison of SMTP and HTTP;
Proxy Server; The Socket Interface;
Outcomes:
At the end of the course, the student should be able to:
15
1. Provide a good conceptual understanding of advance computer networking
2. Understand various models and their functions
3. Advance understanding of performance evaluation
4. Understand network economics
Text Books:
1. William Stallings, “High-Speed Networks and Internets, Performance and Quality of
Service”, Pearson Education;
2. Douglas E. Comer, “Internetworking with TCP/IP Volume – I, Principles, Protocols, and
Architectures”, Fourth Edition, Pearson Education.
Reference Books:
1. B. Muthukumaran, “Introduction to High Performance Networks”, Vijay Nicole Imprints.
2. Wayne Tomasi, “Introduction to Data Communications and Networking”, Pearson
Education.
3. James F. Kurose, Keith W. Ross, “Computer Networking, A Top-Down Approach
Featuring the Internet”, Pearson Education.
4. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education.
5. Behrouz A. Forouzan, “Data Communications and Networking”, Fourth Edition, McGraw
Hill.
Mahbub Hassan, Raj Jain, “High Performance TCP/IP Networking, Concepts, Issues, and
Solutions”, Pearson Education.
16
M.Tech. in Computer Science & Engineering Semester I
MCO 003A Advanced Operating Systems 4-0-0
Course Objective:
• To introduce the state of the art in operating systems and distributed systems, and how to
design modern operating systems.
• To understand how to engage in systems research in general and operating systems
research in particular.
• To investigate novel ideas in operating sytems through a semester-long research project.
Module 1:
Operating System: Definition, Operating System as Resource Manager. Types
of Operating Systems: Simple Batch Processing, Multi-programmed Batch
Processing, Time Sharing, Personal Computer systems, Parallel, Distributed and
Real Time Operating Systems. Operating System Components, Services, Calls,
System Programs, Operating System Structure, Virtual Machines, System
Design and Implementation.
Module 2:
Process Management: Concepts, Scheduling, Operations, Co-operating
processes, Inter-process Communication. Threads: Thread usage, threads in User
Space, threads in Kernel, Hybrid Implementation, Scheduler Activation, Pop-up
threads, Multithreading.
CPU Scheduling: Basic Concepts, Scheduling Criteria, Algorithms, Multiple-
processor Scheduling, Real Time Scheduling, Algorithm Evaluation.
Module 3:
Process Synchronization: Critical Section Problem, Synchronization
Hardware, Semaphores, Classical Problem of synchronization, Critical Regions,
Monitors. Deadlock: Characteristics, Necessary Conditions, Prevention,
Avoidance, Detection and Recovery.
Memory Management: Logical and Physical Address Space, Swapping.
Contiguous Allocation: Singlepartitioned, Multi-partitioned. Non-contiguous
Allocation: Paging, Segmentation, and Segmentation with Paging. Virtual
Memory: Demand Paging, Page Replacement Algorithms, Allocation of Frames,
Thrashing, Demand Segmentation.
Module 4:
File and Directory System: File Concepts, Access Methods, Directory
Structure, Protection, File system Structure, Allocation Methods, Free Space
Management, Directory Implementation, Recovery. Secondary Storage
Management: Disk Structure, Dedicated, Shared, Virtual, Sequential Access
and Random Access Devices, Disk Scheduling, Disk Management, Swap-space
Management, Disk Reliability, Stable Storage Management.
Protection and Security: Threats, Intruders, Accidental Data Loss,
Cryptography, User authentication, Attacks from inside the system, Attacks from
outside the system, Protection Mechanism, Trusted Systems, Domain of
Protection, Access Matrix, Programs Threats, System Threats.
17
Module 5:
Distributed systems, topology network types, design strategies. Network
operating structure, distributed operating system, remote services, and design
issues. Distributed file system: naming and transparency, remote file access,
Stateful v/s Stateless Service, File Replication.
Distributed co-ordinations: Event Ordering, Mutual Exclusion, Atomicity,
Concurrency Control, Deadlock Handling, Election Algorithms, and Reaching
Agreement. Case studies of Unix and MS-DOS operating system.
Outcomes:
At the end of the course, the student should be able to:
1. Understand the state of the art in operating systems and distributed systems, and how to
design modern operating systems.
2. Understand how to engage in systems research in general and operating systems research
in particular.
3. Investigate novel ideas in operating ystems through a semester-long research project.
Suggested Books
1. Silberschatz and Galvin, "Operating System Concepts", Addison-Wesley publishing, Co.,1999.
2. A. S. Tanenbaum, “Modern Operating Systems”, Pearson Education.
3. H.M. Dietel, “An Introduction to Operating System”, Pearson Education.
4. D. M. Dhamdhere, “Operating Systems – A Concept Based Approach”, Tata McGraw-Hill
5 M. Singhal, N. G. Shivaratri, “Advanced Concepts in Operating Systems”, Tata McGraw
-Hill.
6. William Stallings, “Operating Systems”, Pearson Education
18
M.Tech. in Computer Science & Engineering Semester I
MCO 011A Cloud Computing: Course Outlines 4-0-0
Course Objective:
1. To familiarize the philosophy, power, practical use of cloud.
2. To introduce fundamental principles, technology, and techniques of CC
3. To Discuss common problems that can be best solved with/in cloud
4. To Eliminate misconceptions about cloud computing
Module 1:
Understanding cloud computing: Introduction to Cloud Computing - Benefits
and Drawbacks - Types of Cloud Service Development - Deployment models
Module 2:
Cloud Architecture Technology and Architectural Requirements: The
Business Case for Clouds - Hardware and Infrastructure – Accessing the cloud –
Cloud Storage – Standards- Software as a Service – Discovering Cloud Services
Development tools. Three Layered Architectural Requirement - Provider
Requirements
Module 3:
Service Centric Issues - Interoperability - QoS - Fault Tolerance - Data
Management Storage and Processing - Virtualization Management - Scalability
- Load Balancing - Cloud Deployment for Enterprises - User Requirement -
Comparative Analysis of Requirement.
Module 4:
Security Management in Cloud: Security Management Standards - Security
Management in the Cloud Availability Management - SaaS Availability
Management - PaaS Availability Management - IaaS Availability Management
- Access Control - Security Vulnerability, Patch, and Configuration Management
– Privacy in Cloud- The Key Privacy Concerns in the Cloud - Security in Cloud
Computing.
Module 5:
Virtualization: Objectives - Benefits - Virtualization Technologies - Data
Storage Virtualization – Storage Virtualization – Improving Availability using
Virtualization - Improving Performance using Virtualization- Improving
Capacity using Virtualization.
Outcomes:
At the end of the course, the student should be able to:
1. Understand the philosophy, power, practical use of cloud.
2. Present fundamental principles, technology, and techniques of CC
3. Discuss common problems that can be best solved with/in cloud
4. Eliminate misconceptions about cloud computing
Text books:
19
1. David S Linthicum, “Cloud Computing and SOA Convergence in your Enterprise A Step
by Step Guide”, Addison Wesley Information Technology Series.
2. Anthony T Velte, Toby J.Velte, Robert Elsenpeter, “Cloud computing A Practical
Approach “, Tata McGraw Hill Publication
3. Tim Mather, SubraKumaraswamy, ShahedLatif, “Cloud Security and Privacy –
4. An Enterprise Perspective on Risks and Compliance” , O’Reilly Publications, First Edition
5. Michael Miller, “Cloud Computing – Web-Based Applications that Change the Way You
Work and Collaborate Online”, Pearson Education, New Delhi, 2009.
6. Cloud Computing Specialist Certification Kit – Virtualization Study Guide.
20
M.Tech. in Computer Science & Engineering Semester I
MCO 014A Advance Topics inData Mining and Warehousing 3-0-0
Course Objective:
• To compare and contrast different conceptions of data mining as evidenced in both research
and application.
• To explain the role of finding associations in commercial market basket data.
• To characterize the kinds of patterns that can be discovered by association rule mining.
• To describe how to extend a relational system to find patterns using association rules.
UNIT 1:
Overview: Concept of data mining and warehousing, data warehouse roles and
structures, cost of warehousing data, roots of data mining, approaches to data
exploration and data mining, foundations of data mining, web warehousing, web
warehousing for business applications and consumers, introduction to knowledge
management, data warehouses and knowledge bases.
UNIT 2:
Data Warehouse: Theory of data warehousing, barriers to successful data
warehousing, bad data warehousing approaches, stores, warehouse and marts,
data warehouse architecture,metadata, metadata extraction, implementing the
data warehouse and data warehouse technologies.
UNIT 3:
Data Mining and Data Visualisation: Data mining, OLAP, techniques used to
mine the data,market basket analysis, current limitations and challenges to DM,
data visualization.
Designing and Building the Data Warehouse: The enterprise model approach
of data mining design, data warehouse project plan, analysis and design tools,
data warehouse architecture,specification and development.
UNIT 4:
Web-Based Query and Reporting: Delivering information over the web, query
and reporting tools and business value, architectural approaches to delivering
query capabilities over the web.
Web Based Statistical Analysis and Data Mining: Analytical tools, business
value from analytical tools, humble spreadsheet, determining the business value
that analytical tools will deliver, statistical products overview – statistical
analysis applications, correlation analysis,regression analysis, data discovery
tools overview, data discovery applications, comparison of the products,
architectural approaches for statistical and data discovery tools.
UNIT 5:
Search Engines and Facilities: Search engines and the web, search engine
architecture, variations in the way the search facilities work and variations in
indexing schemes.
Future of Data Mining and Data Warehousing: Future of data warehousing,
trends in data warehousing, future of data mining, using data mining to protect
privacy, trends affecting the future of data mining and future of data
visualization.
21
Outcomes:
At the end of the course, students should be able to:
• Compare and contrast different conceptions of data mining as evidenced in both research and
application.
• Explain the role of finding associations in commercial market basket data.
• Characterize the kinds of patterns that can be discovered by association rule mining.
• Describe how to extend a relational system to find patterns using association rules.
• Evaluate methodological issues underlying the effective application of data mining.
Text Books
1. Jiwei Han, MichelienKamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann
Publishers an Imprint of Elsevier, 2001.
Reference Books:
1. ArunK.Pujari, Data Mining Techniques, Universities Press (India) Limited, 2001.
2. George M. Marakas, Modern Data warehousing, Mining and Visualization: core concepts,
Printice Hall, First Edition,2002.
22
M.Tech. in Computer Science & Engineering Semester I
MCO 021A Digital Image Processing 4-0-0
Course Objective
• To cover the basic theory and algorithms that are widely used in digital image processing
• To expose students to current technologies and issues that are specific to image
processing system
• To develop hands-on experience in using computers to process images
• To familiarize with MATLAB Image Processing Toolbox
UNIT 1:
Fundamentals Of Image Processing
Introduction, Elements of visual perception, Steps in Image Processing
Systems, Image Acquisition, Sampling and Quantization, Pixel Relationships,
Colour Fundamentals and Models,File Formats. Introduction to the
Mathematical tools.
UNIT 2:
Image Enhancement and Restoration
Spatial Domain Gray level Transformations Histogram Processing Spatial
Filtering, Smoothing and Sharpening. Frequency Domain: Filtering in
Frequency Domain, DFT, FFT, DCT, Smoothing and Sharpening filters,
Homomorphic Filtering., Noise models, Constrained and Unconstrained
restoration models.
UNIT 3:
Image Segmentation and Feature Analysis
Detection of Discontinuities, Edge Operators, Edge Linking and Boundary
Detection, Thresholding, Region Based Segmentation, Motion Segmentation,
Feature Analysis and Extraction.
UNIT 4:
Multi Resolution Analysis and Compressions
Multi Resolution Analysis: Image Pyramids – Multi resolution expansion –
Wavelet Transforms,
Fast Wavelet transforms, Wavelet Packets. Image Compression: Fundamentals,
Models, Elements of Information Theory, Error Free Compression, Lossy
Compression, Compression Standards JPEG/MPEG.
UNIT 5:
Applications of Image Processing: Representation and Description, Image
Recognition, Image Understanding, Image Classification, Video Motion
Analysis, Image Fusion, Steganography, Colour Image Processing.
Outcomes:
At the end of the course, the student should be able to:
23
• Cover the basic theory and algorithms that are widely used in digital image processing
• Expose students to current technologies and issues that are specific to image processing
system
• Develop hands-on experience in using computers to process images
• Familiarize with MATLAB Image Processing Toolbox .
Text Books:
1. Digital Image Processing - Dr. S.Sridhar Oxford University Press
24
M.Tech. in Computer Science & Engineering Semester I
MCO 016A Information System Security 3-0-0
Course Objective:
• To perform a risk assessment of an information system.
• To identify the security requirements for an information system.
• To use available government information system security resources when designing systems.
UNIT 1:
Introduction to Securities: Introduction to security attacks, services and
mechanism, Classical encryption techniques substitution ciphers and
transposition ciphers, cryptanalysis, steganography, Stream and block ciphers.
Modern Block Ciphers: Block ciphers principles, Shannon’s theory of confusion
and diffusion, fiestal structure, Data encryption standard (DES), Strength of
DES, Idea of differential cryptanalysis, block cipher modes of operations, Triple
DES
UNIT 2:
Modular Arithmetic: Introduction to group, field, finite field of the form GF(p),
modular arithmetic, prime and relative prime numbers, Extended Euclidean
Algorithm, Advanced Encryption Standard (AES) encryption and decryption
Fermat’s and Euler’s theorem, Primality testing, Chinese Remainder theorem,
Discrete Logarithmic Problem, Principals of public key crypto systems, RSA
algorithm, security of RSA
UNIT 3:
Message Authentication Codes: Authentication requirements, authentication
functions, message authentication code, hash functions, birthday attacks, security
of hash functions, Securehash algorithm (SHA)
Digital Signatures: Digital Signatures, Elgamal Digital Signature Techniques,
Digital signature standards (DSS), proof of digital signature algorithm
UNIT 4:
Key Management and distribution: Symmetric key distribution, Diffie-
Hellman Key Exchange, Public key distribution, X.509 Certificates, Public key
Infrastructure.
Authentication Applications: Kerberos
Electronic mail security: pretty good privacy (PGP), S/MIME.
UNIT 5:
IP Security: Architecture, Authentication header, Encapsulating security
payloads, combining security associations, key management. Introduction to
Secure Socket Layer, Secure electronic, transaction (SET).
System Security: Introductory idea of Intrusion, Intrusion detection, Viruses and
related threats,firewalls.
Outcomes:
At the end of the course, students should be able to:
• Perform a risk assessment of an information system.
• Identify the security requirements for an information system.
• Use available government information system security resources when designing systems.
25
Suggested Books:
1. William Stallings, “Cryptography and Network Security: Principals and Practice”,Pearson
Education.
2. Behrouz A. Frouzan: Cryptography and Network Security, TMH
3. Bruce Schiener, “Applied Cryptography”. John Wiley & Sons
4. Bernard Menezes,” Network Security and Cryptography”, Cengage Learning.
5. AtulKahate, “Cryptography and Network Security”, TMH
26
M.Tech. in Computer Science & Engineering Semester I
MCO 008A Advanced Topics in Algorithm Lab 0-0-2
List of Experiments
1. Write a Program to implement Efficient Matrix Multiplication
2. Write a Program to define the graphs and list all nodes and Links
3. Write a Program to implement the concept of BFS
4. Write a Program to implement the concept of DFS
5. Write a Program to implement the concept of B-tree
6. Write a Program to implement Dijkistra Algorithm
7. Write a Program to implement the concept of Binomial Heap
8. Write a program to find Greatest Common Divisor
9. Write a program using Chinese remainder theorem
10 Write program to solve linear equations
11 Write a program to solve Travelling Salesman problem
12 Write a program to implement Vertex cover problem
13 Write a program to implement all pair shortest path Algorithm
27
M.Tech. in Computer Science & Engineering Semester I
MCO 036A Advance Technology lab 0-0-2
The aim of this lab is to introduce the different simulation tools to the students. So that students
get familiar with different simulation environment and implement their theoretical knowledge.
1. Introduction of network Simulator.
2. Experiment Based on Network Simulator.
3. Introduction of OmNet .
4. Experiment Based on OmNet.
5. Introduction of WeKa.
6. Experiment Based on Weka.
7. Introduction based on SimSE.
28
MCO 093A Advance database management system 4-0-0
Course Objective
1. To learn the fundamentals of data models and to conceptualize and depict a database
system using ER diagram.
2. To make a study of SQL and relational database design.
3. To understand the internal storage structures using different file and indexing techniques
which will help in physical DB design.
4. To know the fundamental concepts of transaction processing- concurrency control
techniques and recovery procedure.
UNIT 1:
Relational Databases: Integrity Constraints revisited: Functional, Multi-valued
and Join Dependency, Template Algebraic, Inclusion and Generalized
Functional Dependency, Chase Algorithms and Synthesis of Relational
Schemes. Query Processing and Optimization: Evaluation of Relational
Operations, Transformation of Relational Expressions, Indexing and Query
Optimization, Limitations of Relational Data Model, Null Values and Partial
Information.
UNIT 2:
Deductive Databases:Datalog and Recursion, Evaluation of Datalog program,
Recursive queries with negation. Objected Oriented and Object Relational
Databases: Modeling Complex Data Semantics, Specialization, Generalization,
Aggregation and Association, Objects, Object Identity, Equality and Object
Reference, Architecture of Object Oriented and Object Relational Databases
UNIT 3:
Distributed Data Storage: Fragmentation and Replication, Location and
Fragment Transparency, Distributed Query Processing and Optimization,
Distributed Transaction Modeling and Concurrency Control, Distributed
Deadlock, Commit Protocols, Design of Parallel Databases, Parallel Query
Evaluation.
UNIT 4:
Advanced Transaction Processing: Nested and Multilevel Transactions,
Compensating Transactions and Saga, Long Duration Transactions, Weak
Levels of Consistency, Transaction Work Flows, Transaction Processing
Monitors.
UNIT 5:
Active Databases: Triggers in SQL, Event Constraint and Action: ECA Rules,
Query Processing and Concurrency Control, Compensation and Databases
Recovery. Real Time Databases: Temporal Constraints: Soft and Hard
Constraints, Transaction Scheduling and Concurrency Control.
At the end of the course, the student should be able to:
• Learn the fundamentals of data models and to conceptualize and depict a database system
using ER diagram.
29
• Make a study of SQL and relational database design.
• Understand the internal storage structures using different file and indexing techniques
which will help in physical DB design.
• Know the fundamental concepts of transaction processing- concurrency control
techniques and recovery procedure.
Text Book
1. Abraham Silberschatz, Henry Korth, and S. Sudarshan, Database System Concepts,
McGraw-Hill.
Reference Books:
2. Raghu Ramakrishnan, Database Management Systems, WCB/McGraw-Hill.
3. Bipin Desai, An Introduction to Database Systems, Galgotia.
4. J. D. Ullman, Principles of Database Systems, Galgotia.
5. R. Elmasri and S. Navathe, Fundamentals of Database Systems8, Addison-Wesley.
6. Serge Abiteboul, Richard Hull and Victor Vianu, Foundations of Databases. Addison-
Wesley.
30
MCO 094A Distributed Algorithms 4-0-0
Course Objective
1. To understand synchronous and asynchronous models
2. To learn algorithms of synchronous and asynchronous system
3. To understand shared memory concept of distributed operating system
UNIT 1: Models of synchronous and asynchronous distributed computing systems:
synchronous networks, asynchronous shared memory, asynchronous networks;
UNIT 2:
Basic algorithms for synchronous and asynchronous networks: leader election,
breadth first search, shortest path, minimum spanning tree.
UNIT 3:
Advanced synchronous algorithms: distributed consensus with failures,
commit protocols.
UNIT 4:
Asynchronous shared memory algorithms: mutual exclusion and consensus
UNIT 5:
Relationship between shared memory and network models; asynchronous
networks with failures.
At the end of the course, the student should be able to:
• Understand difference between synchronous and asynchronous system
• Learn algorithms of distributed operating system
• Understand shared memory concepts
• Understand relation between shared memory and network models
Text Book
Nancy Lynch, "Distributed Algorithms" Morgan Kaufmann.
Reference Books:
Gerlad Tel, "Introduction to Distributed Algorithms" Cambridge University Press.
31
HS0001 Research Methodology & Technical
Communication
3-0-0
Course Objective:
• To gain insights into how scientific research is conducted.
• To help in critical review of literature and assessing the research trends, quality and
extension potential of research and equip students to undertake research.
• To learn and understand the basic statistics involved in data presentation.
• To identify the influencing factor or determinants of research parameters.
Module 1:
Research: Meaning & Purpose, Review of literature, Problem
definition/Formulation of research problem, Research proposal, Variables,
Hypothesis, types, construction of hypothesis
Module 2:
Classification of research: Quantitative research: Descriptive Research,
Experimental Research
Qualitative research: Observational studies, Historical research, Focus group
discussion, Case study method,
Module 3:
Sources of data collection: Primary and Secondary Data Collection, Sample and
Sampling technology, Non-probability and Probability Sampling
Module 4:
Tools for data collection: Tests, Interview, Observation, Questionnaire/
Schedule, Characteristics of a good test, Statistics: Descriptive and Inferential
Statistics,Data Analysis, Report Writing, Results and References,
Module 5:
Thesis Writing and Journal Publications: Writing thesis, Writing journal and
conference papers, IEEE and Harvard style of referencing, Effective
presentation, Copyrights, and Avoid plagiarism
Outcome:
At the end of the course, the student should be able to:
• Gain insights into how scientific research is conducted.
• Help in critical review of literature and assessing the research trends, quality and
extension potential of research and equip students to undertake research.
• Learn and understand the basic statistics involved in data presentation.
• Identify the influencing factor or determinants of research parameters.
32
MCO 095A Soft Computing 4-0-0
Course Objective
Module 1:
Introduction: Neural Network-ANN Definition, advantage of Neural network,
Application scope of Neural network, Fuzzy logic, Genetic Algorithms, Hybrid
System, soft computing, Fundamental Concept : Artificial Neural
Network,Biological Neural Network, Evolution of Neural Network, Mcculloch
Pitts Model, Hebb Network, Linear Separability
Module 2:
Supervised learning: perceptron network, Adaptive linear neuron, multiple
Adaptive linear neuron, Back propagation network, Radial Basis Function
Network
Module 3:
Associative Memory Network:Introduction, Training Algorithms for Pattern
Association,Auto associative Memory Network: Hetro Associative Memory
Network, Bidirectional Memory Network, hope Field Network
Module 4:
Unsupervised Learning Networks- Kohonen Self-Organizing Feature Maps,
Learning Vector Quantization, Counterpropagation Networks, Adaptive
Resonance Theory Network
Module 5: Special Networks: Simulated Annealing Network, Gaussian Machine, Cauchy
machine, Probability Neural Net
Text Books
1. S.N. Sivanandam, S.N. Deepa, “Principles of Soft Computing”, 2nd Edition, Wiley, 2011
33
MCO 075A Internet of things 4-0-0
Course Objective
Module
1:
Overview, technology of the internet of things, enchanted objects, Design
principles for connected devices, Privacy, Web thinking for connected devices
Module
2:
Writing Code: building a program and deploying to a device, writing to Actuators,
Blinking Led, Reading from Sensors, Light Switch, Voltage Reader, Device as
HTTP Client, HTTP, Push Versus Pull
Module
3:
Pachube, Netduino, Sending HTTP Requests—the Simple Way, Sending HTTP
Requests—the Efficient Way
Module
4:
HTTP: Device as HTTP Server, Relaying Messages to and from the Netduino,
Request Handlers, Web Html, Handling Sensor Requests, Handling Actuator
Requests
Module
5:
Going Parallel: Multithreading, Parallel Blinker, prototyping online components,
using an API, from prototypes to reality, business models, ethics, privacy,
disrupting control, crowd sourcing
At the end of the course, the student should be able to:
References:
1. Adrian McEwen and Hakim Cassimally, “Designing the Internet of Things”, John
Wiley & Sons, 2013.
2. Cuno Pfister, “Getting Started with the Internet of Things: Connecting Sensors and
Microcontrollers to the Cloud”, Maker Media, 2011.
3. Rob Barton, Gonzalo Salgueiro, David Hanes, “IoT Fundamentals: Networking
Technologies, Protocols, and Use Cases for the Internet of Things”, Cisco Press, 2017.
4. RadomirMihajlovic, Muthu Ramachandran, Reinhold Behringer, PetarKocovic
“Emerging Trends and Applications of the Internet of Things”, IGI Global, 2017.
5. HwaiyuGeng, “Internet of Things and Data Analytics Handbook”, John Wiley & Sons,
2017.
6. Marco Schwartz, “Internet of Things with Arduino Cookbook”, Packt Publishing,
2016.
34
MCO 096A Cyber security and Laws 4-0-0
Course Objective
• To understand about cyber-attacks, crimes
• To understand securities issue
• To understand property rights
• To learn Laws of cyber-crimes
Module 1:
Introduction: Review of TCP/IP and TCP, IP Header analysis, Introduction to
Cyber World, Cyber attacks and cyber security, Information warfare and cyber
terrorism, Types of cyber attacks, Cyber Crime and Digital Fraud, Overview of
Types of computer forensics i.e. Media Forensics, Network forensics (internet
forensics), Machine forensic, Email forensic (e-mail tracing and investigations)
Module 2:
Issues in cyber security: Private ordering solutions, Regulation and
Jurisdiction for global Cyber security, Copy Right-source of risks, Pirates,
Internet Infringement, Fair Use, postings, criminal liability, First Amendments,
Data Loss.
Module 3:
Intellectual property rights: Copy Right-Source of risks, Pirates, Internet
Infringement, Fair Use, postings, Criminal Liability, First Amendments, Losing
Data, Trademarks, Defamation, Privacy-Common Law Privacy, Constitutional
law, Federal Statutes, Anonymity, Technology expanding privacy rights.
Module 4:
Procedural Issues Duty of Care, Criminal Liability, Procedural issues,
Electronic Contracts & Digital Signatures, Misappropriation of information,
Civil Rights, Tax, Evidence.
Module 5:
Legal aspects of cyber security: Ethics, Legal Developments, Late 1990 to
2000,Cyber security in Society, Security in cyber laws case. studies, General
law and Cyber Law-a Swift Analysis.
At the end of the course, the student should be able to:
• Understand different cyber-attacks, crimes
• Understand how to securefrom cyber crimes
• Understand property rights
• Learn Laws of cyber-crimes
35
References:
1. Jonathan Rosenoer,“Cyber Law: The law of the Internet”, Springer-Verlag, 1997.
2. D. Bainbridge, Introduction to Computer Law, 5th Edition, Pearson Education, 2004.
3. P. Duggal, Cyber Law: The Indian Perspective, Saakshar Law Publications, 2005.
4. Mark F Grady, Fransesco Parisi, “The Law and Economics of Cyber Security”, Cambridge
University Press, 2006.
5. S.P. Tripathy, “Cyber security”, Wiley Publications.
36
MCO 036A Advance Technology lab 0-0-2
The aim of this lab is to introduce the different simulation tools to the students. So that students
get familiar with different simulation environment and implement their theoretical knowledge.
8. Introduction of network Simulator.
9. Experiment Based on Network Simulator.
10. Introduction of OmNet .
11. Experiment Based on OmNet.
12. Introduction of WeKa.
13. Experiment Based on Weka.
14. Introduction based on SimSE.
37
MCO 098A Advance Compiler Design 4-0-0
Course Objective
Module 1:
Introduction to Advanced Topics: Review of Compiler Structure, Advanced
Issues in Elementary Topics, The Importance of Code Optimization, Structure
of Optimizing Compilers, Informal Compiler Algorithm Notation (ICAN)
Module 2:
Control-Flow Analysis: Approaches to Control-Flow Analysis, Depth-First
Search, Preorder Traversal, Postorder Traversal, and Breadth-First Search,
Dominators, Loops and Strongly Connected Components, Reducibility, Interval
Analysis and Control Trees, Structural Analysis
Module 3:
Data-Flow Analysis: Basic Concepts: Lattices, Flow Functions, and Fixed
Points, Taxonomy of Data-Flow Problems and Solution Methods, Iterative
Data-Flow Analysis, Lattices of Flow Functions, Control-Tree-Based Data-
Flow Analysis, Structural Analysis, Interval Analysis,
Static Single-Assignment (SSA) Form, Dealing with Arrays, Structures, and
Pointers
Module 4:
Dependence Analysis and Dependence Graphs: Dependence Relations, Basic-
Block Dependence DAGs, Dependences in Loops, Dependence Testing,
Program-Dependence Graphs
Module 5:
Early optimizations: Constant-Expression Evaluation (Constant Folding),
Scalar Replacement of Aggregates, Algebraic Simplifications and
Reassociation, Value Numbering, Copy Propagation, Sparse Conditional
Constant Propagation Redundancy Elimination: Common-Subexpression
Elimination, Loop-Invariant Code Motion, Partial-Redundancy Elimination,
Redundancy Elimination and Reassociation, Code Hoisting
Text Books
1) Advanced Compiler Design and Implementation, by Steven Muchnick, Publisher:
Morgan Kaufmann
Reference Books
1) Engineering a Compiler, by Keith Cooper and Linda Torczon, Publisher: Morgan
Kaufmann
2) Optimizing Compilers for Modern Architectures, by Randy Allen & Ken Kennedy,
Publisher: Morgan Kaufmann.
38
MCO 083A Big Data Security 4-0-0
Course Objective
Module 1:
BIG DATA PRIVACY, ETHICS AND SECURITY
Privacy – Reidentification of Anonymous People – Why Big Data Privacy is self
regulating? – Ethics – Ownership – Ethical Guidelines – Big Data Security –
Organizational Security.
Module 2:
SECURITY, COMPLIANCE, AUDITING, AND PROTECTION
Steps to secure big data – Classifying Data – Protecting – Big Data Compliance
– Intellectual Property Challenge – Research Questions in Cloud Security – Open
Problems.
Module 3:
HADOOPECURITY DESIGN
Kerberos – Default Hadoop Model without security - Hadoop Kerberos Security
Implementation & Configuration.
Module 4:
HADOOP ECOSYSTEM SECURITY
Configuring Kerberos for Hadoop ecosystem components – Pig, Hive, Oozie,
Flume, HBase, Sqoop.
Module 5:
DATA SECURITY & EVENT LOGGING
Integrating Hadoop with Enterprise Security Systems - Securing Sensitive Data
in Hadoop – SIEM system – Setting up audit logging in hadoop cluster
At the end of the course, the student should be able to:
References:
1. Mark Van Rijmenam, “Think Bigger: Developing a Successful Big Data Strategy for
Your Business”, Amazon, 1 edition, 2014.
2. Frank Ohlhorst John Wiley & Sons, “Big Data Analytics: Turning Big Data into
Big Money”, John Wiley & Sons, 2013.
3. SherifSakr, “Large Scale and Big Data: Processing and Management”, CRC Press, 2014.
4. Sudeesh Narayanan, “Securing Hadoop”, Packt Publishing, 2013.
5. Ben Spivey, Joey Echeverria, “Hadoop Security Protecting Your Big Data
Problem”, O’Reilly Media, 2015.
6. Top Tips for Securing Big Data Environments: e-book
(http://www.ibmbigdatahub.com/whitepaper/top-tips-securing-big-data-
environments-e-book)
7. http://www.dataguise.com/?q=securing-hadoop-discovering-and-securing-sensitive-
data-hadoop-data-stores
40
MCO 099A HUMAN INTERFACE SYSTEM DESIGN 4-0-0
Course Objective:
• To understand Design process management
• To understand Interaction devices and windows strategies
• To understand Managing virtual environments
UNIT 1:
INTRODUCTION
Goals of System Engineering – Goals of User Interface Design – Motivations
of Human factors in Design – High Level Theories –Object-Action Interface
Design - Three Principles – Guidelines for Data Display and Data Entry
UNIT 2:
MANAGING DESIGN PROCESS
Introduction- Organizational Design to Support Usability – The Three Pillars of
Design-Development Methodologies- Ethnographic Observation – Participating
Design- Scenario Development- Social Impact Statement for Early Design –
Legal Issues- Reviews – Usability Testing and laboratories- Surveys-
Acceptance tests – Evaluation during Active use- Specification Methods-
Interface – Building Tools- Evaluation and Critiquing tools
UNIT 3:
MANIPULATION AND VIRTUAL ENVIRONMENTS
Introduction-Examples of Direct Manipulation Systems –Explanation of Direct
Manipulation-Visual Thinking and Icons – Direct manipulation Programming –
Home Automation- Remote Direct Manipulation- Virtual Environments- Task-
Related Organization – Item Presentation Sequence- Response Time and
Display Rate – Fast Movement Through Menus- Menu Layouts- Form Fillin –
Dialog Box – Functionality to Support User’s Tasks – Command Organization
Strategies – Benefits of Structure- Naming and Abbreviations – Command
Menus- Natural Language in Computing.
UNIT 4:
INTERACTION DEVICES
Introduction – Keyboards and Functions – Pointing Devices- Speech
recognition ,Digitization and Generation – Image and Video Displays – Printers
–Theoretical Foundations –Expectations and Attitudes – User Productivity –
Variability – Error messages – Nonanthropomorphic Design –Display Design –
color-Reading from Paper versus from Displays- Preparation of Printed
Manuals- Preparation of Online Facilities.
UNIT 5:
WINDOWS STRATEGIES AND INFORMATION SEARCH
Introduction- Individual Widow Design- Multiple Window Design-
Coordination by Tightly –Coupled Widow- Image Browsing- Personal Role
Management and Elastic Windows – Goals of Cooperation – Asynchronous
Interaction – Synchronous Distributed – Face to Face- Applying Computer
Supported Cooperative Work to Education – Database query and phrase search
in Textual documents – Multimedia Documents Searches – Information
Visualization – Advance Filtering Hypertext and Hypermedia – World Wide
Web- Genres and Goals and Designers – Users and their tasks – Object Action
Interface Model for Web site Design
Outcomes
At the end of the course, students should be able to:
41
Text Books:
1. Ben Shneiderman , " Designing the User Interface”, 3rd Edition, Addison-Wesley, 2001
Reference Books:
1. Barfied , Lon , “The User Interface : Concepts and Design", Addison – Wesley
2.Wilbert O. Galiz , “The Essential guide to User Interface Design”, Wiley Dreamtech, 2002
3.Jacob Nielsen, " Usability Engineering ", Academic Press, 1993.
4.Alan Dix et al, " Human - Computer Interaction ", Prentice Hall, 1993.
42
MCO 087A Machine Learning 4-0-0
Course Objective:
• To understand the machine learning concepts.
• To understand the roles of machine learning.
• To explain different models of machine learning.
• To understand the genetic algorithms and reinforcement learning
UNIT 1:
INTRODUCTION – Well defined learning problems, Designing a Learning
System, Issues in Machine Learning;
THE CONCEPT LEARNING TASK - General-to-specific ordering of
hypotheses, Find-S, List then eliminate algorithm, Candidate elimination
algorithm, Inductive bias
UNIT 2:
DECISION TREE LEARNING - Decision tree learning algorithm-Inductive
bias- Issues in Decision tree learning;
ARTIFICIAL NEURAL NETWORKS – Perceptrons, Gradient descent and
the Delta rule, Adaline, Multilayer networks, Derivation of backpropagation
rule Backpropagation Algorithm-Convergence, Generalization;
UNIT 3:
EVALUATING HYPOTHESES – Estimating Hypotheses Accuracy, Basics
of sampling Theory, Comparing Learning Algorithms;
BAYESIAN LEARNING – Bayes theorem, Concept learning, Bayes Optimal
Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm;
UNIT 4:
COMPUTATIONAL LEARNING THEORY – Sample Complexity for
Finite Hypothesis spaces, Sample Complexity for Infinite Hypothesis spaces,
The Mistake Bound Model of Learning;
INSTANCE-BASED LEARNING – k-Nearest Neighbor Learning, Locally
Weighted Regression, Radial basis function networks, Case-based learning
UNIT 5:
GENETIC ALGORITHMS – an illustrative example, Hypothesis space
search, Genetic Programming, Models of Evolution and Learning; Learning
first order rules-sequential covering algorithms-General to specific beam
search-FOIL
REINFORCEMENT LEARNING - The Learning Task, Q Learning.
Outcomes
At the end of the course, students should be able to:
• Understand the machine learning concepts.
• Understand the roles of machine learning.
• Explain different models of machine learning.
• Understand the genetic algorithm and reinforcement learning
Text Books:
4. Tom.M.Mitchell, Machine Learning, McGraw Hill International Edition
Reference Books:
1. Ethern Alpaydin, Introduction to Machine Learning. Eastern Economy Edition,
PrenticeHall of India, 2005.
43
2. Bishop, C., Pattern Recognition and Machine Learning. Berlin: Springer-
Verlag.Ian Foster and Carl Kesselman, “Grid Blue Print for New
Computing Infrastructure”,Morgan Kaufmann, 2000.
44
MCO 024A Grid Computing 4-0-0
Course Objective:
• To understand the grid computing concepts.
• To understand the roles of Grid computing.
• To explain Grid architecture.
• To understand the Grid related technologies
UNIT 1:
Grid Computing: values and risks – History of Grid computing, Grid computing
model and protocols, Overview and types of Grids.
UNIT 2:
Desktop Grids : Background, Definition, Challenges, Technology, Suitability,
Grid server and practical uses, Clusters and Cluster Grids, HPC Grids, Scientific
in sight, Application and Architecture, HPC application, Development
Environment and HPC Grids, Data Grids, Alternatives to Data Grid, Data Grid
architecture.
UNIT 3:
The open Grid services Architecture, Analogy, Evolution, Overview, Building
on the OGSA platform, Implementing OGSA based Grids, Creating and
Managing services, Services and the Grid, Service Discovery, Tools and
Toolkits, Universal Description Discovery and Integration
UNIT 4:
Desktop Supercomputing, Parallel Computing, Parallel Programming
Paradigms, Problems of Current parallel Programming Paradigms, Desktop
Supercomputing Programming Paradigms, Parallelizing Existing Applications,
Grid Enabling Software Applications, Needs of the Grid users, methods of Grid
Deployment, Requirements for Grid enabling Software, Grid Enabling Software
Applications.
UNIT 5:
Application integration, Application classification, Grid requirements,
Integrating applications with Middleware platforms, Grid enabling Network
services, Managing Grid environments, Managing Grids, Management reporting,
Monitoring, Data catalogs and replica management, Portals, Different
application areas of Grid computing.
Outcomes
At the end of the course, students should be able to:
• Understand the grid computing concepts.
• Understand the roles of Grid computing.
• Explain Grid architecture.
• Understand the Grid related technologies
Text Books:
1. Ahmar Abbas, “Grid Computing: A Practical Guide to Technology and
Applications”,Firewall Media, 2004.
45
Reference Books:
1. Joshy Joseph and Craig Fellenstein, “Grid Computing”, Pearson Education, 2001.
2. Ian Foster and Carl Kesselman, “Grid Blue Print for New Computing Infrastructure”,Morgan
Kaufmann, 2000.
3. Fran Berman, Geoffrey Fox and Anthony J. G. Hey, “Grid Computing: Making the
GlobalInfrastructure a Reality”, Willy Publisher, 2001
46
MCO 100A Fuzzy logic 4-0-0
Course Objective:
• Fuzzy sets and representations
• Fuzzy Relation and Logic
• Fuzzy systems and Application
UNIT 1:
INTRODUCTION
Uncertainty and imprecision-statistics and random processes-uncertainty in
information- fuzzy sets and membership-classical sets-operations on classical
sets –properties of classical sets-fuzzy set operations-properties of fuzzy sets.
UNIT 2:
FUZZY RELATIONS AND MEMBERSHIP FUNCTIONS
Brief about Crisp relations- fuzzy relations –fuzzy tolerance and equivalence
relations-value assignments-membership functions-features-standandard forms
and boundaries-fuzzification –membership value assignments –inference-rank
ordering-neural networks-genetic algorithms –inductive reasoning.
UNIT 3:
FUZZIFICZTION AND FUZZY ARITHMETIC
Lambda-cuts for fuzzy sets-lambda cutsfor fuzzy relations- defuzzification
methods-Extension principle-functions of fuzzy sets- fuzzy transform-fuzzy
numbers-approximate methods of extension-vertex method-DSW algorithm
UNIT 4:
FUZZY LOGIC AND FUZZY RULE BASED SYSTEMS 9
Fuzzy logic–approximate reasoning-fuzzy tautologies-contradictions-
equivalence-and logical proofs-other forms of implication operation and
composition operation-linguistic hedges-rule based systems-fuzzy associative
memories-multiobjective decision making –fuzzy bayesian decision method.
UNIT 5:
APPLICATIONS
Single sample identification-multifeature pattern recognition-image processing-
simple fuzzy logic controllers-General fuzzy logic controllers-Industrial
applications-Fuzzy tool box in Matlab.
Outcomes
At the end of the course, students should be able to:
• Understand the Fuzzy sets and representations
• Understand the Fuzzy Relation and Logic
• Understand the Fuzzy systems and Application
Text Books:
Timothy J.Ross, ’’Fuzzy Logic with Engineering applications”,McGraw Hill Inc.Reference
Books:
1.George j.Klir & Tina A.Folger, “Fuzzy sets Uncertainty & Information”,PHI,2001
2.J.S.R.Jang C.T.Sun,E.Mizutani,”Neuro fuzzy and Soft Computing”,PHI,2003
47
MCO 026A Biometric Security 4-0-0
Course Objective:
• To explain different biometrics parameters
• To design a basic biometric facility
• To participate in Bidding process and Equipment installation of Biometric Equipment
• To Administrate a Biometric Facility
UNIT 1: Explain the errors generated in biometric measurements cs: Need
UNIT 2:
Conventional techniques of authentication, challenges – legal and privacy issues
UNIT 3:
Biometrics in use: DNA, fingerprint, Iris, Retinal scan, Face, hand geometry
UNIT 4:
Human gait, speech, ear. Handwriting, Keystroke dynamics
UNIT 5:
Signature Multimodal biometrics: Combining biometrics, scaling issues.
Biometric template security
Outcomes
At the end of this course Students will be able to:
• Explain different biometrics parameters
• Design a basic biometric facility
• Participate in Bidding process and Equipment installation of Biometric Equipment
• Administrate a Biometric Facility
• Understand the privacy challenges of Biometrics
Texts/References:
1. Julian D. M. Ashbourn, Biometrics: Advanced Identify Verification: The Complete
Guide
Reference Books:
1. DavideMaltoni (Editor), et al, Handbook of Fingerprint Recognition
2. L.C. Jain (Editor) et al, Intelligent Biometric Techniques in Fingerprint and Face Recognition
3. John Chirillo, Scott Blaul, Implementing Biometric Security
48
MCO 101A Neural Networks Programming Techniques 4-0-0
Course Objective:
• To explain basic concepts of neuron networks
• To explain multiple models of neural networks
• To understand applications of neural netwoks
UNIT 1:
Basics of ANN: Models to Neuron; Basic learning laws. Activation and Synaptic
Dynamics:Activation dynamics models; Synaptic dynamics models; Stability
and Convergence.
UNIT 2:
Analysis of Feed forward Neural Networks: Linear associative networks for
patternassociation; Single layer and Multilayer Perception network for pattern
classification; Multilayerfeed forward neural networks for pattern mapping
UNIT 3:
Analysis of Feedback Neural Networks: Linear auto associative networks;
Hopfield model for pattern storage; stochastic networks; Boltzmann machine
for pattern environment storage.
UNIT 4:
Competitive Learning Neural Networks: Basic competitive learning laws;
Analysis of patternclustering networks; Analysis of self-organizing feature
mapping networks
UNIT 5: Applications of ANN: Pattern classification problems; Optimization; Control.
Outcomes
At the end of this course Students will be able to:
• Explain basic concepts of neural networks
• Explain different feed forward networks
• Explain different feedback networks
• Explain competitive learning
• Understand applications of ANNTexts/References:
1. J.A. Anderson, An Introduction to Neural Networks, MIT
Reference Books:
2. Hagen Demuth Beale, Neural Network Design, Cengage Learning
3. Laurene V. Fausett, "Fundamentals of Neural Networks : Architectures, Algorithms and
Applications", Pearson India
4. Kosko, Neural Network and Fuzzy Sets, PHI
50
MCO 006A Client Server Programming 4-0-0
Course Objective:
• To aware of the characteristics of client-server computing,
• To understand the issues associated with client-server computing,
• To know the basic approaches for implementing client-server computations via the
TCP/IP suite.
UNIT 1:
1. Concurrent Processing in Client-Server software: Introduction,
Concurrency in Networks, Concurrency in Servers, Terminology and Concepts,
An example of Concurrent Process Creation, Executing New Code, Context
Switching and Protocol Software Design, Concurrency and Asynchronous I/O.
2. Program Interface to Protocols: Introduction, Loosely Specified Protocol
Software Interface, Interface Functionality, Conceptual Interface Specification,
System Calls, Two Basic Approaches to Network Communication, The Basic
I/O Functions available in UNIX, Using UNIX I/O with TCP/IP.
UNIT 2:
The Socket Interface: Introduction, Berkley Sockets, Specifying a Protocol
Interface, The Socket Abstraction, Specifying an End Point Address, A Generic
Address Structure, Major System Calls used with Sockets, Utility Routines for
Integer Conversion, Using Socket Calls in a Program, Symbolic Constants for
Socket Call Parameters. Algorithms and Issues in Client Software
Design: Introduction, Learning Algorithms instead of Details, Client
Architecture, Identifying the Location of a Server, Parsing an Address
Argument, Looking up a Domain Name, Looking up a well-known Port by
Name, Port Numbers and Network Byte Order, Looking up a Protocol by Name,
The TCP Client Algorithm, Allocating a Socket, Choosing a Local Protocol Port
Number, A fundamental Problem in choosing a Local IP Address, Connecting
a TCP Socket to a Server, Communicating with the Server using TCP, Reading
a response from a TCP Connection, Closing a TCP Connection, Programming
a UDP Client, Connected and Unconnected UDP Socket, Using Connect with
UDP, Communicating with a Server using UDP, Closing a Socket that uses
UDP, Partial Close for UDP, A Warning about UDP Unreliability.
UNIT 3:
Algorithms and Issues in Server Software Design: Introduction, The
Conceptual Server Algorithm, Concurrent Vs Iterative Servers, Connection-
Oriented Vs Connectionless Access, Connection-Oriented Servers,
Connectionless Servers, Failure, Reliability and Statelessness, Optimizing
Stateless Servers, Four Basic Types of Servers, Request Processing Time,
Iterative Server Algorithms, An Iterative Connection-Oriented Server
Algorithm, Binding to a Well Known Address using INADDR_ANY, Placing
the Socket in Passive Mode, Accepting Connections and using them. An
Iterative Connectionless Server Algorithm, Forming a Reply Address in a
Connectionless Server, Concurrent Server Algorithms, Master and Slave
51
Processes, A Concurrent Connectionless Server Algorithm, A concurrent
Connection-Oriented Server Algorithm, Using separate Programs as Slaves,
Apparent Concurrency using a Single Process, When to use each Server Types,
The Important Problem of Server Deadlock, Alternative Implementations.
UNIT 4:
Iterative, Connectionless Servers (UDP): Introduction, Creating a Passive
Socket, Process Structure, An example TIME Server.
Iterative, Connection-Oriented Servers (TCP): Introduction, Allocating a
Passive TCP Socket, A Server for the DAYTIME Service, Process Structure,
An Example DAYTIME Server, Closing Connections, Connection Termination
and Server Vulnerability
UNIT 5:
Concurrent, Connection-Oriented Servers (TCP): Introduction, Concurrent
ECHO, Iterative Vs Concurrent Implementations, Process Structure, An
example Concurrent ECHO Server, Cleaning up Errant Processes
Outcomes:
At the end of the course, the student should be able to:
• Aware of the characteristics of client-server computing,
• Understand the issues associated with client-server computing,
• Know the basic approaches for implementing client-server computations via the TCP/IP suite
TEXT BOOK:
1. Douglas E.Comer, David L. Stevens: Internetworking with TCP/IP – Vol. 3, Client-Server
Programming and Applications, BSD Socket Version with ANSI C, 2nd Edition, Pearson, 2001.
liam Stallings, “Operating Systems”, Pearson Education
52
School of Engineering & Technology
Syllabi and Course Structure
M. Tech. in Cloud Computing
(Computer Science)
Academic Programmes
April, 2019
53
The main objective of program is to prepare the students to become the Cloud professionals. The
objective of this programme is to create the skilled professionals who will solve the industry
problem and develop the Cloud infrastructure. The curriculum of this programis designed to
provide maximum industry exposure and several practical credits that provide hands-on
capabilities on the various aspects of cloud.
Upon successful completion of the program, students may find excellent carrier opportunities in
research-oriented industries and top ranking global companies and may start their career as Cloud
Solution Architects, Cloud System Administrator and Cloud Security Specialist.
54
School of Engineering & Technology
M.Tech. inComputer Science & Engineering (CLOUD COMPUTING)
Course Structure
First Semester
First Semester
Sub Code Sub Name L T P C
MCO 056A Advanced Data Structure and Algorithm Design 4 0 0 4
MCO 007A Advance Data Communication network 4 0 0 4
MCO 003A Advanced Operating Systems 4 0 0 4
MCO 011A Cloud Computing
Elective I
4 0 0 4
MCO 014A Advance Topics in Data Mining
and warehousing
MCO 021A Digital Image Processing
MCO 016A Information system security
MCO 008A Advanced Data Structure and Algorithm Lab 0 0 2 2
MCO 018A Advance Technology Lab 0 0 2 2
MCO 010A Seminar 0 0 2 2
TOTAL 16 0 06 22
55
School of Engineering & Technology
M.Tech. inComputer Science & Engineering (CLOUD COMPUTING)
Second Semester
SECOND SEMESTER
Sub Code Sub Name L T P C
MCO 102A Cloud Storage Infrastructures 4 0 0 4
MCO 103A Cloud Strategy Planning & Management 4 0 0 4
Research Methodology 3 0 0 3
MCO 104A Cloud Security
Elective II
4 0 0 4
MCO 105A Managing Virtual
Environment
MCO 106A Data Science & Big Data
Analytics
Quantitative Techniques & Computer
Applications Lab
0 0 1 1
MCO 107A Cloud Storage Infrastructures lab 0 0 2 2
MCO 018A Advance Technology Lab 0 0 2 2
MCO 019A Project 0 0 2 2
TOTAL 15 0 07 22
56
School of Engineering & Technology
M.Tech. inComputer Science & Engineering (CLOUD COMPUTING)
Third Semester
THIRD SEMESTER
Sub Code Sub Name L T P C
Sub Code Sub Name L T P C
MCO 108A Cloud Architectures
4 0 0 4
MCO 109A Design & Development of Cloud with
Openstack
4 0 0 4
MCO110A Cloud Application
Development
Elective III
4 0 0 4
MCO111A Data Center Virtualization
MCO 112A IOT in Clouds
MCO 113A Big-Data Analytics for Cloud
Elective IV
4 0 0 4
MCO 114A Design & Development of
Cloud Applications
MCO 115A Converged Networks
MCO 029A Dissertation-I 0 0 0 12
TOTAL 16 0 0 28
Fourth Semester
FOURTH SEMESTER
MCO 030A Dissertation-II 0 0 0 28
TOTAL 0 0 0 28
57
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 007A Advanced Data Communication Network 4-0-0
Course Objective
5. To provide a good conceptual understanding of advance computer networking
6. To understand various models and their functions
7. To have an advance understanding of performance evaluation
8. To understand network economics
Module 1:
The Motivation for Internetworking; Need for Speed and Quality of Service;
History of Networking and Internet; TCP/IP and ATM Networks; Internet
Services; TCP Services; TCP format and connection management;
Encapsulation in IP; UDP Services, Format and Encapsulation in IP; IP Services;
Header format and addressing; Fragmentation and reassembly; classless and
subnet address extensions; sub netting and super netting; CIDR; IPv6;
Module 2:
Congestion Control and Quality of Service: Data traffic; Network performance;
Effects of Congestion; Congestion Control; Congestion control in TCP and
Frame Relay; Link-Level Flow and Error Control; TCP flow control; Quality of
Service: Flow Characteristics, Flow Classes; Techniques to improve QoS;
Traffic Engineering; Integrated Services;
Module 3:
High Speed Networks: Packet Switching Networks; Frame Relay Networks;
Asynchronous Transfer Mode (ATM); ATM protocol Architecture; ATM logical
connections; ATM cells; ATM Service categories; ATM Adaptation Layer;
Optical Networks: SONET networks; SONET architecture;
Wireless WANs: Cellular Telephony; Generations; Cellular Technologies in
different generations; Satellite Networks;
Module 4:
Internet Routing: Interior and Exterior gateway Routing Protocols; Routers and
core routers; RIP; OSPF; BGP; IDRP; Multicasting; IGMP; MOSPF; Routing in
Ad Hoc Networks; Routing in ATM: Private Network-Network Interface;
Module 5:
Error and Control Messages: ICMP; Error reporting vs Error Correction; ICMP
message format and Delivery; Types of messages;
Address Resolution (ARP); BOOTP; DHCP; Remote Logging; File Transfer and
Access; Network Management and SNMP; Comparison of SMTP and HTTP;
Proxy Server; The Socket Interface;
Outcomes:
58
At the end of the course, the student should be able to:
5. Provide a good conceptual understanding of advance computer networking
6. Understand various models and their functions
7. Advance understanding of performance evaluation
8. Understand network economics
Text Books:
3. William Stallings, “High-Speed Networks and Internets, Performance and Quality of
Service”, Pearson Education;
4. Douglas E. Comer, “Internetworking with TCP/IP Volume – I, Principles, Protocols, and
Architectures”, Fourth Edition, Pearson Education.
Reference Books:
6. B. Muthukumaran, “Introduction to High Performance Networks”, Vijay Nicole Imprints.
7. Wayne Tomasi, “Introduction to Data Communications and Networking”, Pearson
Education.
8. James F. Kurose, Keith W. Ross, “Computer Networking, A Top-Down Approach
Featuring the Internet”, Pearson Education.
9. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education.
10. Behrouz A. Forouzan, “Data Communications and Networking”, Fourth Edition, McGraw
Hill.
Mahbub Hassan, Raj Jain, “High Performance TCP/IP Networking, Concepts, Issues, and
Solutions”, Pearson Education.
59
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 003A Advanced Operating Systems 4-0-0
Course Objective:
• To introduce the state of the art in operating systems and distributed systems, and how to
design modern operating systems.
• To understand how to engage in systems research in general and operating systems
research in particular.
• To investigate novel ideas in operating sytems through a semester-long research project.
Module 1:
Operating System: Definition, Operating System as Resource Manager. Types
of Operating Systems: Simple Batch Processing, Multi-programmed Batch
Processing, Time Sharing, Personal Computer systems, Parallel, Distributed and
Real Time Operating Systems. Operating System Components, Services, Calls,
System Programs, Operating System Structure, Virtual Machines, System
Design and Implementation.
Module 2:
Process Management: Concepts, Scheduling, Operations, Co-operating
processes, Inter-process Communication. Threads: Thread usage, threads in User
Space, threads in Kernel, Hybrid Implementation, Scheduler Activation, Pop-up
threads, Multithreading.
CPU Scheduling: Basic Concepts, Scheduling Criteria, Algorithms, Multiple-
processor Scheduling, Real Time Scheduling, Algorithm Evaluation.
Module 3:
Process Synchronization: Critical Section Problem, Synchronization
Hardware, Semaphores, Classical Problem of synchronization, Critical Regions,
Monitors. Deadlock: Characteristics, Necessary Conditions, Prevention,
Avoidance, Detection and Recovery.
Memory Management: Logical and Physical Address Space, Swapping.
Contiguous Allocation: Singlepartitioned, Multi-partitioned. Non-contiguous
Allocation: Paging, Segmentation, and Segmentation with Paging. Virtual
Memory: Demand Paging, Page Replacement Algorithms, Allocation of Frames,
Thrashing, Demand Segmentation.
Module 4:
File and Directory System: File Concepts, Access Methods, Directory
Structure, Protection, File system Structure, Allocation Methods, Free Space
Management, Directory Implementation, Recovery. Secondary Storage
Management: Disk Structure, Dedicated, Shared, Virtual, Sequential Access
and Random Access Devices, Disk Scheduling, Disk Management, Swap-space
Management, Disk Reliability, Stable Storage Management.
Protection and Security: Threats, Intruders, Accidental Data Loss,
Cryptography, User authentication, Attacks from inside the system, Attacks from
outside the system, Protection Mechanism, Trusted Systems, Domain of
Protection, Access Matrix, Programs Threats, System Threats.
60
Module 5:
Distributed systems, topology network types, design strategies. Network
operating structure, distributed operating system, remote services, and design
issues. Distributed file system: naming and transparency, remote file access,
Stateful v/s Stateless Service, File Replication.
Distributed co-ordinations: Event Ordering, Mutual Exclusion, Atomicity,
Concurrency Control, Deadlock Handling, Election Algorithms, and Reaching
Agreement. Case studies of Unix and MS-DOS operating system.
Outcomes:
At the end of the course, the student should be able to:
4. Understand the state of the art in operating systems and distributed systems, and how to
design modern operating systems.
5. Understand how to engage in systems research in general and operating systems research
in particular.
6. Investigate novel ideas in operating ystems through a semester-long research project.
Suggested Books
1. Silberschatz and Galvin, "Operating System Concepts", Addison-Wesley publishing, Co.,1999.
2. A. S. Tanenbaum, “Modern Operating Systems”, Pearson Education.
3. H.M. Dietel, “An Introduction to Operating System”, Pearson Education.
4. D. M. Dhamdhere, “Operating Systems – A Concept Based Approach”, Tata McGraw-Hill
5 M. Singhal, N. G. Shivaratri, “Advanced Concepts in Operating Systems”, Tata McGraw
-Hill.
6. William Stallings, “Operating Systems”, Pearson Education
61
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 011A Cloud Computing: Course Outlines 4-0-0
Course Objective:
5. To familiarize the philosophy, power, practical use of cloud.
6. To introduce fundamental principles, technology, and techniques of CC
7. To Discuss common problems that can be best solved with/in cloud
8. To Eliminate misconceptions about cloud computing
Module 1:
Understanding cloud computing: Introduction to Cloud Computing - Benefits
and Drawbacks - Types of Cloud Service Development - Deployment models
Module 2:
Cloud Architecture Technology and Architectural Requirements: The
Business Case for Clouds - Hardware and Infrastructure – Accessing the cloud –
Cloud Storage – Standards- Software as a Service – Discovering Cloud Services
Development tools. Three Layered Architectural Requirement - Provider
Requirements
Module 3:
Service Centric Issues - Interoperability - QoS - Fault Tolerance - Data
Management Storage and Processing - Virtualization Management - Scalability
- Load Balancing - Cloud Deployment for Enterprises - User Requirement -
Comparative Analysis of Requirement.
Module 4:
Security Management in Cloud: Security Management Standards - Security
Management in the Cloud Availability Management - SaaS Availability
Management - PaaS Availability Management - IaaS Availability Management
- Access Control - Security Vulnerability, Patch, and Configuration Management
– Privacy in Cloud- The Key Privacy Concerns in the Cloud - Security in Cloud
Computing.
Module 5:
Virtualization: Objectives - Benefits - Virtualization Technologies - Data
Storage Virtualization – Storage Virtualization – Improving Availability using
Virtualization - Improving Performance using Virtualization- Improving
Capacity using Virtualization.
Outcomes:
At the end of the course, the student should be able to:
5. Understand the philosophy, power, practical use of cloud.
6. Present fundamental principles, technology, and techniques of CC
7. Discuss common problems that can be best solved with/in cloud
8. Eliminate misconceptions about cloud computing
Text books:
62
7. David S Linthicum, “Cloud Computing and SOA Convergence in your Enterprise A Step
by Step Guide”, Addison Wesley Information Technology Series.
8. Anthony T Velte, Toby J.Velte, Robert Elsenpeter, “Cloud computing A Practical
Approach “, Tata McGraw Hill Publication
9. Tim Mather, SubraKumaraswamy, ShahedLatif, “Cloud Security and Privacy –
10. An Enterprise Perspective on Risks and Compliance” , O’Reilly Publications, First Edition
11. Michael Miller, “Cloud Computing – Web-Based Applications that Change the Way You
Work and Collaborate Online”, Pearson Education, New Delhi, 2009.
12. Cloud Computing Specialist Certification Kit – Virtualization Study Guide.
63
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 014A Advance Topics in Data Mining and Warehousing 3-0-0
Course Objective:
• To compare and contrast different conceptions of data mining as evidenced in both research
and application.
• To explain the role of finding associations in commercial market basket data.
• To characterize the kinds of patterns that can be discovered by association rule mining.
• To describe how to extend a relational system to find patterns using association rules.
UNIT 1:
Overview: Concept of data mining and warehousing, data warehouse roles and
structures, cost of warehousing data, roots of data mining, approaches to data
exploration and data mining, foundations of data mining, web warehousing, web
warehousing for business applications and consumers, introduction to knowledge
management, data warehouses and knowledge bases.
UNIT 2:
Data Warehouse: Theory of data warehousing, barriers to successful data
warehousing, bad data warehousing approaches, stores, warehouse and marts,
data warehouse architecture,metadata, metadata extraction, implementing the
data warehouse and data warehouse technologies.
UNIT 3:
Data Mining and Data Visualisation: Data mining, OLAP, techniques used to
mine the data,market basket analysis, current limitations and challenges to DM,
data visualization.
Designing and Building the Data Warehouse: The enterprise model approach
of data mining design, data warehouse project plan, analysis and design tools,
data warehouse architecture,specification and development.
UNIT 4:
Web-Based Query and Reporting: Delivering information over the web, query
and reporting tools and business value, architectural approaches to delivering
query capabilities over the web.
Web Based Statistical Analysis and Data Mining: Analytical tools, business
value from analytical tools, humble spreadsheet, determining the business value
that analytical tools will deliver, statistical products overview – statistical
analysis applications, correlation analysis,regression analysis, data discovery
tools overview, data discovery applications, comparison of the products,
architectural approaches for statistical and data discovery tools.
UNIT 5:
Search Engines and Facilities: Search engines and the web, search engine
architecture, variations in the way the search facilities work and variations in
indexing schemes.
Future of Data Mining and Data Warehousing: Future of data warehousing,
trends in data warehousing, future of data mining, using data mining to protect
privacy, trends affecting the future of data mining and future of data
visualization.
Outcomes:
64
At the end of the course, students should be able to:
• Compare and contrast different conceptions of data mining as evidenced in both research and
application.
• Explain the role of finding associations in commercial market basket data.
• Characterize the kinds of patterns that can be discovered by association rule mining.
• Describe how to extend a relational system to find patterns using association rules.
• Evaluate methodological issues underlying the effective application of data mining.
Text Books
1. Jiwei Han, MichelienKamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann
Publishers an Imprint of Elsevier, 2001.
Reference Books:
1. ArunK.Pujari, Data Mining Techniques, Universities Press (India) Limited, 2001.
2. George M. Marakas, Modern Data warehousing, Mining and Visualization: core concepts,
Printice Hall, First Edition,2002.
65
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 021A Digital Image Processing 4-0-0
Course Objective
• To cover the basic theory and algorithms that are widely used in digital image processing
• To expose students to current technologies and issues that are specific to image
processing system
• To develop hands-on experience in using computers to process images
• To familiarize with MATLAB Image Processing Toolbox
UNIT 1:
Fundamentals Of Image Processing
Introduction, Elements of visual perception, Steps in Image Processing
Systems, Image Acquisition, Sampling and Quantization, Pixel Relationships,
Colour Fundamentals and Models,File Formats. Introduction to the
Mathematical tools.
UNIT 2:
Image Enhancement and Restoration
Spatial Domain Gray level Transformations Histogram Processing Spatial
Filtering, Smoothing and Sharpening. Frequency Domain: Filtering in
Frequency Domain, DFT, FFT, DCT, Smoothing and Sharpening filters,
Homomorphic Filtering., Noise models, Constrained and Unconstrained
restoration models.
UNIT 3:
Image Segmentation and Feature Analysis
Detection of Discontinuities, Edge Operators, Edge Linking and Boundary
Detection, Thresholding, Region Based Segmentation, Motion Segmentation,
Feature Analysis and Extraction.
UNIT 4:
Multi Resolution Analysis and Compressions
Multi Resolution Analysis: Image Pyramids – Multi resolution expansion –
Wavelet Transforms,
Fast Wavelet transforms, Wavelet Packets. Image Compression: Fundamentals,
Models, Elements of Information Theory, Error Free Compression, Lossy
Compression, Compression Standards JPEG/MPEG.
UNIT 5:
Applications of Image Processing: Representation and Description, Image
Recognition, Image Understanding, Image Classification, Video Motion
Analysis, Image Fusion, Steganography, Colour Image Processing. Outcomes:
At the end of the course, the student should be able to:
• Cover the basic theory and algorithms that are widely used in digital image processing
• Expose students to current technologies and issues that are specific to image processing
system
• Develop hands-on experience in using computers to process images
• Familiarize with MATLAB Image Processing Toolbox .
66
Text Books:
1. Digital Image Processing - Dr. S.Sridhar Oxford University Press
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 016A Information System Security 3-0-0
Course Objective:
• To perform a risk assessment of an information system.
• To identify the security requirements for an information system.
• To use available government information system security resources when designing systems.
UNIT 1:
Introduction to Securities: Introduction to security attacks, services and
mechanism, Classical encryption techniques substitution ciphers and
transposition ciphers, cryptanalysis, steganography, Stream and block ciphers.
Modern Block Ciphers: Block ciphers principles, Shannon’s theory of confusion
and diffusion, fiestal structure, Data encryption standard (DES), Strength of
DES, Idea of differential cryptanalysis, block cipher modes of operations, Triple
DES
UNIT 2:
Modular Arithmetic: Introduction to group, field, finite field of the form GF(p),
modular arithmetic, prime and relative prime numbers, Extended Euclidean
Algorithm, Advanced Encryption Standard (AES) encryption and decryption
Fermat’s and Euler’s theorem, Primality testing, Chinese Remainder theorem,
Discrete Logarithmic Problem, Principals of public key crypto systems, RSA
algorithm, security of RSA
UNIT 3:
Message Authentication Codes: Authentication requirements, authentication
functions, message authentication code, hash functions, birthday attacks, security
of hash functions, Securehash algorithm (SHA)
Digital Signatures: Digital Signatures, Elgamal Digital Signature Techniques,
Digital signature standards (DSS), proof of digital signature algorithm
UNIT 4:
Key Management and distribution: Symmetric key distribution, Diffie-
Hellman Key Exchange, Public key distribution, X.509 Certificates, Public key
Infrastructure.
Authentication Applications: Kerberos
Electronic mail security: pretty good privacy (PGP), S/MIME.
UNIT 5:
IP Security: Architecture, Authentication header, Encapsulating security
payloads, combining security associations, key management. Introduction to
Secure Socket Layer, Secure electronic, transaction (SET).
System Security: Introductory idea of Intrusion, Intrusion detection, Viruses and
related threats,firewalls.
67
Outcomes:
At the end of the course, students should be able to:
• Perform a risk assessment of an information system.
• Identify the security requirements for an information system.
• Use available government information system security resources when designing systems.
Suggested Books:
1. William Stallings, “Cryptography and Network Security: Principals and Practice”,Pearson
Education.
2. Behrouz A. Frouzan: Cryptography and Network Security, TMH
3. Bruce Schiener, “Applied Cryptography”. John Wiley & Sons
4. Bernard Menezes,” Network Security and Cryptography”, Cengage Learning.
5. AtulKahate, “Cryptography and Network Security”, TMH
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 008A Advanced Topics in Algorithm Lab 0-0-2
List of Experiments
1. Write a Program to implement Efficient Matrix Multiplication
2. Write a Program to define the graphs and list all nodes and Links
3. Write a Program to implement the concept of BFS
4. Write a Program to implement the concept of DFS
5. Write a Program to implement the concept of B-tree
6. Write a Program to implement Dijkistra Algorithm
7. Write a Program to implement the concept of Binomial Heap
8. Write a program to find Greatest Common Divisor
9. Write a program using Chinese remainder theorem
10 Write program to solve linear equations
11 Write a program to solve Travelling Salesman problem
12 Write a program to implement Vertex cover problem
13 Write a program to implement all pair shortest path Algorithm
68
MCO 036A Advance Technology lab 0-0-2
The aim of this lab is to introduce the different simulation tools to the students. So that students
get familiar with different simulation environment and implement their theoretical knowledge.
15. Introduction of network Simulator.
16. Experiment Based on Network Simulator.
17. Introduction of OmNet .
18. Experiment Based on OmNet.
19. Introduction of WeKa.
20. Experiment Based on Weka.
21. Introduction based on SimSE.
Cloud Computing
Unit-I
Introduction: Historical development ,Vision of Cloud Computing, Characteristics of cloud
computing as per NIST , Cloud computing reference model ,Cloud computing environments,
Cloud services requirements, Cloud and dynamic infrastructure, Cloud Adoption and
rudiments.Overview of cloud applications: ECG Analysis in the cloud, Protein structure
prediction, Gene Expression Data Analysis ,Satellite Image Processing ,CRM and ERP ,Social
networking .
Unit-II
Cloud Computing Architecture: Cloud Reference Model, Types of Clouds, Cloud Interoperability
& Standards, Scalability and Fault Tolerance, Cloud Solutions: Cloud Ecosystem, Cloud Business
Process Management, Cloud Service Management.
Cloud Offerings: Cloud Analytics, Testing Under Control, Virtual Desktop Infrastructure.
Unit –III
69
Cloud Management & Virtualization Technology: Resiliency, Provisioning, Asset management,
Conceps of Map reduce, Cloud Governance, High Availability and Disaster Recovery.
Virtualization: Fundamental concepts of compute ,storage, networking, desktop and application
virtualization .Virtualization benefits, server virtualization, Block and file level storage
virtualization Hypervisor management software, Infrastructure Requirements , Virtual
LAN(VLAN) and Virtual SAN(VSAN) and their benefits .
Unit-IV
Cloud Security: Cloud Information security fundamentals, Cloud security services, Design
principles, Secure Cloud Software Requirements, Policy Implementation, Cloud Computing
Security Challenges, Virtualization security Management, Cloud Computing Secutity
Architecture .
Unit-V
Market Based Management of Clouds , Federated Clouds/Inter Cloud: Characterization &
Definition ,Cloud Federation Stack , Third Party Cloud Services .
Case study : Google App Engine, Microsoft Azure , Hadoop , Amazon , Aneka
Text Book:
1. Buyya, Selvi ,” Mastering Cloud Computing “,TMH Pub
2. Kumar Saurabh, “Cloud Computing” , Wiley Pub
Refrence book:
1. Krutz , Vines, “Cloud Security “ , Wiley Pub
2. Velte, “Cloud Computing- A Practical Approach” ,TMH Pub
3. Sosinsky, “ Cloud Computing” , Wiley Pub
70
Cloud Storage Infrastructure
UNIT I VIRTUALIZED DATA CENTER ARCHITECTURE: Cloud
infrastructures; public, private, hybrid. Service provider interfaces;
Saas, Paas, Iaas. VDC environments; concept, planning and
design, business continuity and disaster recovery principles.
Managing VDC and cloud environments and infrastructures.
UNIT II INFORMATION STORAGE SECURITY & DESIGN: Storage
strategy and governance; security and regulations. Designing
secure solutions; the considerations and implementations involved.
Securing storage in virtualized and cloud environments.
Monitoring and management; security auditing and SIEM.
UNIT III STORAGE NETWORK DESIGN: Architecture of storage,
analysis and planning. Storage network design considerations;
NAS and FC SANs, hybrid storage networking technologies
(iSCSI, FCIP, FCoE), design for storage virtualization in cloud
computing, host system design considerations.
UNIT IV OPTIMIZATION OF CLOUD STORAGE: Global storage
management locations, scalability, operational efficiency. Global
storage distribution; terabytes to petabytes and greater. Policy
based information management; metadata attitudes; file systems or
object storage.
UNIT V INFORMATION AVAILABILITY DESIGN: Designing
backup/recovery solutions to guarantee data availability in a
virtualized environment. Design a replication solution, local
remote and advanced. Investigate Replication in NAS and SAN
environments. Data archiving solutions; analyzing compliance and
archiving design considerations.
TEXT
1. Greg Schulz, “Cloud and Virtual Data Storage Networking”, Auerbach Publications [ISBN: 978-
1439851739], 2011.
REFRENCE:
71
1. Marty Poniatowski, “Foundations of Green IT” Prentice Hall; 1 edition [ISBN: 978-0137043750],
2009.
2. EMC, “Information Storage and Management” Wileyedition [ISBN: 978- 0470294215],2012.
3. Volker Herminghaus, Albrecht Scriba, “Storage Management in Data Centers” Springer; editioN
[ISBN: 978-3540850229]. 2009.
4. Klaus Schmidt, “High Availability and Disaster Recovery” Springer; edition [ISBN: 978-
3540244608], 2006.
72
CLOUD STRATEGY PLANNING & MANAGEMENT
UNIT I ACHIEVING BUSINESS VALUE FROM IT
TRANSFORMATION :Moving to a cloud architecture and
strategy to achieve business value. BPM, IS, Porter’s Value
chain model and BPR as a means of delivering business value;
Developing Business Strategy: Investigate business strategy
models to gain competitive advantage for organizations,
SWOT/PEST, Economies of scale, Porter’s 3 Strategies and 5
Competitive Forces, D’Aveni’s hyper competition models.
UNIT II STRATEGIC IT LEADERSHIP IN THE ORGANIZATION :
Emphasize the roles of the strategic IS/IT leaders such as Chief
Information Officer (CIO) and the Chief Technology Officer
(CTO) in planning and managing IT Strategic development in
the organization
UNIT III PLANNING A CLOUD COMPUTING BASED IT
STRATEGY : Develop an IT strategy to deliver on strategic
business objectives in the business strategy. IT Project planning
in the areas of ITaaS, SaaS, PaaS and IaaS are essential in
delivering a successful strategic IT Plan.
UNIT IV SOA AND BUSINESS AGILITY:Shared services delivered by a
Service Oriented Architecture (SOA) in a Private or Public
Cloud. Services, Databases and Applications on demand. The
effect on Enterprise Architecture and its traditional frameworks
such as Zachman and The Open Group Architecture Framework
(TOGAF).
UNIT V BENEFIT REALIZATION AND IT
GOVERNANCE :Managing resources (people, process,
technology), to realize benefit from Private/Public Cloud IT
services (IaaS, PaaS, PraaS, SaaS), Gartner's 5 pillars of benefit
realization. IT governance as a service in measuring the delivery
73
of IT Strategy from Cloud IT Services using Sarbannes Oxley
(CobiT) and other commonly-used approaches
TEXT
1. Arnold J Cummins, “Easiest Ever Guide to Strategic IT Planning”
http://strategicitplanningguide.com/.
REFRENCE
1. Andy Mulholland, Jon Pyke, Peter Finger, “Enterprise Cloud Computing - A Strategy Guide for Business and
Technology Leaders”, Meghan Kiffer [ISBN: 0929652290],2010.
2 David S. Linthicum, “Cloud Computing and SOA Convergence in Your Enterprise”, Addison Wesley [ISBN:
0136009220], 2009.
3. Charles Babcock, “Management Strategies for the Cloud Revolution”, 1st Ed., Tata McGraw/Hill [ISBN:
0071740759],2010.
4. Mark I. Williams, “A Quick Start Guide to Cloud Computing: Moving Your Business into the Cloud” Kogan Page
[ISBN: 0749461306],2010.
5. Website: “Whitepapers and news for the CIO” www.cio.com.
6. Website: “Gartner Research Website” www.gartner.com
74
Cloud Security
Unit I Cloud Computing Fundamentals :What Cloud Computing
Isn’t ,Alternative Views ,Essential Characteristics ,On-Demand Self-
Service ,Broad Network Access ,Location-Independent Resource
Pooling ,Rapid Elasticity ,Measured Service, Architectural
Influences ,High-Performance Computing ,Utility and Enterprise Grid
Computing ,Autonomic Computing ,Service Consolidation, Horizontal
Scaling ,Web Services ,High-Scalability Architecture, Technological
Influences ,Universal Connectivity, Commoditization, Excess
Capacity ,Open-Source Software ,Virtualization ,Operational Influences
Unit II Cloud Computing Software Security Fundamentals:
Cloud Information Security Objectives: Confidentiality, Integrity, and
Availability, Cloud Security Services: Authentication, Authorization,
Auditing, Accountability, Relevant Cloud Security Design Principles:
Least Privilege, Separation of Duties, Defense in Depth, Fail Safe,
Economy of Mechanism, Complete Mediation, Open Design, Least
Common Mechanism, Psychological Acceptability, Weakest Link,
Leveraging Existing Components
Secure Cloud Software Requirements: Secure Development Practices,
Handling Data, Code Practices, Language Options, Input Validation and
Content Injection, Physical Security of the System
Approaches to Cloud Software Requirements Engineering: A Resource
Perspective on Cloud Software Security Requirements, Goal-Oriented
Software Security Requirements, Monitoring Internal and External
Requirements
Unit III Cloud Security Policy Implementation and Decomposition:
Implementation Issues, Decomposing Critical Security Issues into
Secure Cloud Software Requirements, NIST Security Principles
75
Secure Cloud Software Testing: Testing for Security Quality
Assurance, Conformance Testing, Functional Testing, Performance
Testing, Security Testing
Cloud Penetration Testing: Legal and Ethical Implications, The Three
Pre-Test Phases, Penetration Testing Tools and Techniques, Regression
Testing
Cloud Computing and Business Continuity Planning/Disaster
Recovery: Definitions, General Principles and Practices , Disaster
Recovery Planning, Business Continuity Planning ,Using the Cloud for
BCP/DRP
Unit IV Cloud Computing Risk Issues 125
The CIA Triad: Confidentiality,Integrity,Availability,Other Important
Concepts
Privacy and Compliance Risks:The Payment Card Industry Data
Security Standard (PCI DSS),Information Privacy and Privacy Laws
Threats to Infrastructure: Data, and Access Control, CommonThreats
and Vulnerabilities, Logon Abuse, Inappropriate System
Use,Eavesdropping,Network Intrusion,enial-of-Service (DoS) Attacks,
Session Hijacking Attacks, Fragmentation Attacks
Cloud Access Control Issues:Database Integrity Issues
Cloud Service Provider Risks:Back-Door,Spoofing,Man-in-the-
Middle,Replay,TCP Hijacking, SocialEngineering, DumpsterDiving,
PasswordGuessing, Trojan Horses and Malware
Unit V Cloud Computing Security Challenges
Security Policy Implementation, Policy Types, Senior Management
Statement of Policy ,Regulatory Policies, Advisory
Policies ,Informative Policies ,Computer Security Incident Response
Team (CSIRT)
76
Virtualization Security Management, Virtual Threats, Hypervisor
Risks ,Increased Denial of Service Risk
VM Security Recommendations, Best Practice Security
Techniques,VM-Specifi c Security Techniques, Hardening the Virtual
Machine, Securing VM Remote Access
Text Book:
1. Cloud Security: A Comprehensive guide to secure cloud computing by Ronald L. Krutz
and Russell Dean Vines
Managing Virtual Environment
UNIT I PERFORMANCE MANAGEMENT IN A VIRTUAL
ENVIRONMENT : Management techniques, methodology and
key performance metrics used to identifying CPU, memory,
network, virtual machine and application performance
bottlenecks in a virtualized environment.
UNIT II CONFIGURATION AND CHANGE MANAGEMENT :
Configuration and change management goals and guidelines,
tools and technologies in virtualized environments
UNIT III SECURE VIRTUAL NETWORKING : Configuration and
change management goals and guidelines, tools and
technologies in virtualized environments; Virtual network
security architecture, network segmentation and traffic isolation
to secure a virtual network configuration
UNIT IV PROTECTING THE MANAGEMENT
ENVIRONMENT:Server authentication, authorization, and
accounting, SSL certificates, server hardening; Protecting the
host system: security architecture, controlling access to storage,
hardening hosts, Hardening virtual machines; Virtual machine
security architecture, security parameters; Protecting the host
and virtual machine systems using server authentication,
authorization, and accounting techniques.
77
UNIT V TROUBLESHOOTING VIRTUAL ENVIRONMENTS :
Interpreting host, network, storage, cluster and virtual machine
log files. Network troubleshooting, traffic sniffing, storage
access problems, iSCSI authentication and digests. Virtual
machine migration, cluster errors with shares, pools, and limits;
Command line interfaces and syntax, interpreting host,
network, storage, cluster, virtual machine log files and network
traces.
TEXT
1. Massimo Cafaro (Editor), Giovanni Aloisio (Editor), “Grids, Clouds and Virtualization” Springer; edition [ISBN: 978-
0857290489] 2011.
2. Chris Wolf and Erick M. Halter, “Virtualization” A press; 1 edition [ISBN: 978- 1590594957] 2005.
REFERENCES
3. Gaurav Somani, “Scheduling and Isolation in Virtualization”, VDM Verlag Dr. Müller [ISBN: 978-3639295139],
Muller Publishers, Germany, Sept. 2010
4. LatifaBoursas (Editor), Mark Carlson (Editor), Wolfgang Hommel (Editor), Michelle Sibilla (Editor), KesWold
(Editor), “Systems and Virtualization Management: Standards and New Technologies” [ISBN: 978-3540887072],
October 14, 2008
5. Edward L. Haletky, “VMware ESX Server in the enterprise” [ISBN: 978- 0132302074]. Prentice Hall; 1 edition 29
Dec 2007.
6. Edward Haletky, “VMware ESX and ESXi in the Enterprise - Planning Deployment of Virtualization Servers” [ISBN:
978-0137058976]., Prentice Hall; 2 edition February 18, 2011.
CLOUD ARCHITECTURE
UNIT I CLOUD COMPUTING FUNDAMENTALS: Cloud Computing
definition, private, public and hybrid cloud. Cloud types; IaaS,
PaaS, SaaS. Benefits and challenges of cloud computing, public
vs private clouds, role of virtualization in enabling the cloud;
Business Agility: Benefits and challenges to Cloud architecture.
Application availability, performance, security and disaster
recovery; next generation Cloud Applications
78
UNIT II CLOUD APPLICATIONS:Technologies and the processes
required when deploying web services; Deploying a web service
from inside and outside a cloud architecture, advantages and
disadvantages
UNIT III MANAGEMENT OF CLOUD SERVICES : Reliability,
availability and security of services deployed from the cloud.
Performance and scalability of services, tools and technologies
used to manage cloud services deployment; Cloud Economics :
Cloud Computing infrastructures available for implementing
cloud based services. Economics of choosing a Cloud platform
for an organization, based on application requirements, economic
constraints and business needs (e.g Amazon, Microsoft and
Google, Salesforce.com, Ubuntu and Redhat)
UNIT IV APPLICATION DEVELOPMENT: Service creation
environments to develop cloud based applications. Development
environments for service development; Amazon, Azure, Google
App.
UNIT V CLOUD IT MODEL:Analysis of Case Studies when deciding to
adopt cloud computing architecture. How to decide if the cloud is
right for your requirements. Cloud based service, applications and
development platform deployment so as to improve the total cost
of ownership (TCO
TEXT BOOK:
1.Gautam Shroff, “Enterprise Cloud Computing Technology Architecture Applications”, Cambridge University Press;
1 edition, [ISBN: 978-0521137355], 2010.
REFRENCE BOOK:
1. Toby Velte, Anthony Velte, Robert Elsenpeter, “Cloud Computing, A Practical Approach” McGraw-Hill Osborne
Media; 1 edition [ISBN: 0071626948], 2009.
2. Dimitris N. Chorafas, “Cloud Computing Strategies” CRC Press; 1 edition [ISBN: 1439834539],2010.
DESIGN AND DEVELOPMENT OF CLOUD APPLICATION
UNIT I DESIGNING CLOUD BASED APPLICATIONS :Role of business
analyst, requirements gathering, UML, use of state diagrams,
79
wire frame prototypes, use of design tools such as Balsamiq.
Selecting front end technologies and standards, Impact of growth
in mobile computing on functional design and technology
decisions
UNIT II CLOUD APPLICATION DEVELOPMENT :Technical
architecture considerations – concurrency, speed and
unpredictable loads. Agile development, team composition
(including roles/responsibilities), working with changing
requirements and aggressive schedules. Understanding Model
View Controller (MVC). Advanced understanding of “views”,
location, and the presentation layer: Advanced Ajax and JQuery.
Presenting to different browsers and devices. Localization and
internationalization; Understanding client location and device
type. Mobile application development – Android, iOS, WP, RIM,
Symbian
UNIT III STORING OBJECTS IN THE CLOUD (5 Hours) Session
management. Advanced database techniques using MySQL and
SQL Server, blob storage, table storage. Working with Third
Party APIs: Overview of interconnectivity in cloud ecosystems.
Working with Twitter API, Flickr API, Google Maps API.
Advanced use of JSON and REST.
UNIT IV CLOUD APPLICATIONS AND SECURITY ISSUES :
Understanding cloud based security issues and threats (SQL
query injections, common hacking efforts), SSL, encrypted query
strings, using encryption in the database. Authentication and
identity. Use of oAuth. OpenID; Understanding QA and Support:
Common support issues with cloud apps: user names and
passwords, automated emails and spam, browser variants and
configurations. Role of developers in QA cycle. QA techniques
and technologies. Use of support forums, trouble ticketing.
UNIT V USE CASES :Design, develop and deploy an advanced cloud app
using framework and platform of choice to demonstrate an
understanding of database, presentation and logic. Application
should demonstrate integration with third party API, sensitivity to
geography of user (language, currency, time and date format),
80
authentication of user, security, and awareness of client
device/browser. Case Studies: Salesforce, Basecamp, Xero.com,
Dropbox
TEXT
1. Jim Webber, Savas Parastatidis, Ian Robinson, “REST in Practice” O'Reilly Media; 1 edition, [ISBN:
978-0596805821] 2010.
REFRENCE
1. Eugenio Pace, Dominic Betts, Scott Densmore, Ryan Dunn, Masashi Narumoto, MatiasWoloski, “Developing
Applications for the Cloud on the Microsoft Windows Azure Platform” Microsoft Press; 1 edition, [ISBN:
9780735656062] 2010.
2. Dan Wellman, “jQuery UI 1.6” Packt Publishing [ISBN: 9781847195128] 2009.
3. Peter Lubbers, Brian Albers, Frank Salem, Ric Smith, “Pro HTML5 Programming” A press, [ISBN: 9781430227908]
2010.
3. Lee Babin, “Beginning Ajax with PHP” A press; 1 edition, [ISBN: 9781590596678] 2006.
4. Richard York, “Beginning JavaScript and CSS development with jQuery”, Wiley Pub. Indianapolis, IN [ISBN:
9780470227794] 2009.
5. Edward Benson, “The art of Rails”, Wiley Pub. Indianapolis, IN [ISBN: 9780470189481] 2008.
DATA CENTER VIRTUALIZATION
UNIT I DATA CENTER CHALLENGES (9 Hours) How server, desktop,
network Virtualization and cloud computing reduce data center
footprint, environmental impact and power requirements by
driving server consolidation; Evolution of Data Centers: The
evolution of computing infrastructures and architectures from
standalone servers to rack optimized blade servers and unified
computing systems (UCS).
UNIT II ENTERPRISE-LEVEL VIRTUALIZATION :Provision, monitoring
and management of a virtual datacenter and multiple enterprise-
level virtual servers and virtual machines through software
management interfaces; Networking and Storage in Enterprise
Virtualized Environments - Connectivity to storage area and IP
networks from within virtualized environments using industry
standard protocols
81
UNIT III VIRTUAL MACHINES & ACCESS CONTROL: Virtual machine
deployment, modification, management; monitoring and migration
methodologies.
UNIT IV RESOURCE MONITORING: Physical and virtual machine
memory, CPU management and abstraction techniques using a
hypervisor.
UNIT V VIRTUAL MACHINE DATA PROTECTION :Backup and recovery
of virtual machines using data recovery techniques; Scalability -
Scalability features within Enterprise virtualized environments
using advanced management applications that enable clustering,
distributed network switches for clustering, network and storage
expansion; High Availability : Virtualization high availability and
redundancy techniques.
TEXT
1. Mickey Iqbal, “IT Virtualization Best Practices: A Lean, Green Virtualized Data Center Approach”,
MC Press [ISBN: 978-1583473542] 2010.
REFRENCE
1. Mike Laverick, “VMware vSphere 4 Implementation” Tata McGraw-Hill Osborne Media; 1 edition [ISBN: 978-
0071664523], 2010.
2. Jason W. McCarty, Scott Lowe, Matthew K. Johnson, “VMware vSphere 4 Administration Instant Reference”
Sybex; 1 edition [ISBN: 978- 0470520727],2009.
3. Brian Perry, Chris Huss, Jeantet Fields, “VCP VMware Certified Professional on vSphere 4 Study Guide” Sybex; 1
edition [ISBN: 978-0470569610], 2009.
4. Jason Kappel, Anthony Velte, Toby Velte, “Microsoft Virtualization with Hyper-V: Manage Your Datacenter with
Hyper-V, Virtual PC, Virtual Server, and Application Virtualization” McGraw-Hill Osborne [ISBN: 978-
0071614030],2009
CLOUD APPLICATION DEVELOPMENT
UNIT I CLOUD BASED APPLICATIONS (4 Hours) Introduction,
Contrast traditional software development and development for
the cloud. Public v private cloud apps. Understanding Cloud
ecosystems – what is SaaS/PaaS, popular APIs, mobile
UNIT II DESIGNING CODE FOR THE CLOUD (8 Hours) Class and
Method design to make best use of the Cloud infrastructure; Web
82
Browsers and the Presentation Layer- Understanding Web
browsers attributes and differences. Building blocks of the
presentation layer: HTML, HTML5, CSS, Silverlight, and Flash.
34 SRM-M.Tech Cloud Computing 2015 – 16
UNIT III WEB DEVELOPMENT TECHNIQUES AND FRAMEWORKS (8
Hours) Building Ajax controls, introduction to Javascript using
JQuery, working with JSON, XML, REST. Application
development Frameworks e.g. Ruby on Rails , .Net, Java API's or
JSF; Deployment Environments – Platform As A Service
(PAAS) ,Amazon, vmForce, Google App Engine, Azure, Heroku,
AppForce
UNIT IV USE CASE 1: BUILDING AN APPLICATION USING THE LAMP
STACK (4 Hours) Setting up a LAMP development environment.
Building a simple Web app demonstrating an understanding of the
presentation layer and connectivity with persistence.
UNIT V USE CASE 2: DEVELOPING AND DEPLOYING AN
APPLICATION IN THE CLOUD (6 Hours) Building on the
experience of the first project students will study the design,
development, testing and deployment of an application in the
cloud using a development framework and deployment platform
TEXT
1. Chris Hay, Brian Prince, “Azure in Action” Manning Publications [ISBN: 978- 1935182481],2010
REFRENCE:
1.. Henry Li, “Introducing Windows Azure” Apress; 1 edition [ISBN: 978-1-4302- 2469-3],2009.
2. Eugenio Pace, Dominic Betts, Scott Densmore, Ryan Dunn, Masashi Narumoto, MatiasWoloski, “Developing
Applications for the Cloud on the Microsoft Windows Azure Platform” Microsoft Press; 1 edition [ISBN:
9780735656062],2010.
3. Eugene Ciurana, “Developing with Google App Engine” Apress; 1 edition [ISBN: 978-1430218319],2009.
4. Charles Severance, “Using Google App Engine” O'Reilly Media; 1 edition, [ISBN: 978-0596800697], 2009.
5. George Reese, “Cloud application architectures”, O'Reilly Sebastopol, CA [ISBN: 978-0596156367] 2009.
6. Dan Sanderson, “Programming Google App Engine” O'Reilly Media; 1 edition [ISBN: 978-0596522728],2009.
7. Paul J. Deitel, Harvey M. Deitel, “Ajax, rich Internet applications, and web development for programmers”,
Prentice Hall Upper Saddle River, NJ [ISBN: 978-0-13-158738-0], 2008.
83
Faculty of Engineering & Technology
Syllabi and Course Structure
M. Tech. in Artificial Intelligence
85
Faculty of Engineering & Technology
M.Tech. inComputer Science & Engineering (Artificial Intelligence) Course Structure
First Semester
First Semester
Sub Code Sub Name L T P C
MCO 056 B Advanced Data Structure and Algorithm
Design
3 0 0 3
MCO 007B Advance Data Communication Network 3 0 0 3
MCO 003B Advanced Operating Systems 3 0 0 3
MCO 021B Advanced Data Mining & Warehousing 3 0 0 3
MCO 070B Advanced Data Structure and Algorithm
Lab
0 0 2 1
MCO 036B Advanced Technology Lab 0 0 2 1
Research Methodology 2 0 0 2
Research Methodology Lab 0 0 2 1
Open Elective - 1 3 0 0 3
TOTAL 17 0 6 20
86
Faculty of Engineering & Technology
M.Tech. in Computer Science & Engineering (Artificial Intelligence)
Second Semester
SECOND SEMESTER
Sub Code Sub Name L T P C
Principle of Artificial Intelligence & Machine
Learning
3 0 0 3
Pattern Recognition 3 0 0 3
Knowledge Engineering and Expert Systems 3 0 0 3
ANN & Deep Learning 3 0 0 3
Pattern Recognition Lab 0 0 2 1
Artificial Neural Network & Deep Learning
Lab
0 0 2 1
Project 0 0 6 3
TOTAL 12 0 10 17
87
Faculty of Engineering & Technology
M.Tech. in Computer Science & Engineering (Artificial Intelligence)
Third Semester
THIRD SEMESTER
Sub Code Sub Name L T P C
Application of AI in Industry 3 0 0 3
Open Elective - 2 3 0 0 3
Dissertation-I 0 0 18 9
TOTAL 16 0 18 15
Fourth Semester
FOURTH SEMESTER
MCO 030A Dissertation-II 0 0 32 16
TOTAL 0 0 32 16
88
MCO 056A Advanced Data Structure and Algorithms Design 3-0-0
Course Objective
• To understand the various algorithm design technique.
• To learn analysis techniques to analyze the algorithms.
• To understand the advanced data structures, intrinsic complexity analysis, problem
settings
UNIT 1
Advanced Data Structure: Graph, B-tree, binomial heaps and, Fibbonacci
heap, Red black tree
UNIT 2
Graph Algorithms: Single source shortest paths-Belman-Ford algorithm,
Dijkistra algorithm, all pairs shortest path and matrix multiplication, Floyad-
Warshal lalhm, Johnson algorithm for parse graph, maximum flow-Ford-
Fulkuson method and maximum bipartite matching.
UNIT 3
Number Theoretic Algorithm: GCD, modular arithmetic, solving modular
linear equation and Chinese remainder theorem.
Amortized Analysis, Data Structures for Disjoint Sets
UNIT 4
NP Completeness: Polynomial time, polynomial time verification, NP
completeness and reducibility, Cook’s theorem, NP complete problems-clique
problem, vertex cover problem, approximation algorithms-vertex cover problem,
set covering problem, traveling salesman problem.
UNIT 5 Probabilistic Algorithms: Numerical probabilistic algorithm, Monte-Carlo
algorithm and Las-Vegas algorithm,Sorting network
Text Books:
1. Cormen T.H., Leiserson C.E., Rivest R.L., Introduction to Algorithms, Prentice Hall of
India
Refrence Books:
1. Brassad G. &Bratley P., Fundamentals of Algorithmics , Prentice Hall of India
Course Outcomes:
CO1. Understand the various algorithm design technique.
CO2. Learn analysis techniques to analyze the algorithms.
CO3. Understand the advanced data structures, intrinsic complexity analysis, problem settings
MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES:
Course
Outcome
Program Outcome Program
Specific
Outcome
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 H H M H M M
90
M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester I
MCO 007A Advanced Data Communication Network 3-0-0
Course Objective
9. To provide a good conceptual understanding of advance computer networking
10. To understand various models and their functions
11. To have an advance understanding of performance evaluation
12. To understand network economics
Module 1:
The Motivation for Internetworking; Need for Speed and Quality of Service;
History of Networking and Internet; TCP/IP and ATM Networks; Internet
Services; TCP Services; TCP format and connection management;
Encapsulation in IP; UDP Services, Format and Encapsulation in IP; IP Services;
Header format and addressing; Fragmentation and reassembly; classless and
subnet address extensions; sub netting and super netting; CIDR; IPv6;
Module 2:
Congestion Control and Quality of Service: Data traffic; Network performance;
Effects of Congestion; Congestion Control; Congestion control in TCP and
Frame Relay; Link-Level Flow and Error Control; TCP flow control; Quality of
Service: Flow Characteristics, Flow Classes; Techniques to improve QoS;
Traffic Engineering; Integrated Services;
Module 3:
High Speed Networks: Packet Switching Networks; Frame Relay Networks;
Asynchronous Transfer Mode (ATM); ATM protocol Architecture; ATM logical
connections; ATM cells; ATM Service categories; ATM Adaptation Layer;
Optical Networks: SONET networks; SONET architecture;
Wireless WANs: Cellular Telephony; Generations; Cellular Technologies in
different generations; Satellite Networks;
Module 4:
Internet Routing: Interior and Exterior gateway Routing Protocols; Routers and
core routers; RIP; OSPF; BGP; IDRP; Multicasting; IGMP; MOSPF; Routing in
Ad Hoc Networks; Routing in ATM: Private Network-Network Interface;
Module 5:
Error and Control Messages: ICMP; Error reporting vs Error Correction; ICMP
message format and Delivery; Types of messages;
Address Resolution (ARP); BOOTP; DHCP; Remote Logging; File Transfer and
Access; Network Management and SNMP; Comparison of SMTP and HTTP;
Proxy Server; The Socket Interface;
91
Text Books:
5. William Stallings, “High-Speed Networks and Internets, Performance and Quality of
Service”, Pearson Education;
6. Douglas E. Comer, “Internetworking with TCP/IP Volume – I, Principles, Protocols, and
Architectures”, Fourth Edition, Pearson Education.
7.
Reference Books:
11. B. Muthukumaran, “Introduction to High Performance Networks”, Vijay Nicole Imprints.
12. Wayne Tomasi, “Introduction to Data Communications and Networking”, Pearson
Education.
13. James F. Kurose, Keith W. Ross, “Computer Networking, A Top-Down Approach
Featuring the Internet”, Pearson Education.
14. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education.
15. Behrouz A. Forouzan, “Data Communications and Networking”, Fourth Edition, McGraw
Hill.
16. Mahbub Hassan, Raj Jain, “High Performance TCP/IP Networking, Concepts, Issues, and
Solutions”, Pearson Education.
Course Outcomes:
CO1. Provide a good conceptual understanding of advance computer networking
CO2. Understand and compare various models and their functions
CO3. Advance understanding and evaluating the performance of network
CO4. Understand network economics /Compare and contrast various Network protocols
MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES:
Course
Outcome
Program Outcome Program
Specific
Outcome
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 H H M M
CO2 H H M M
CO3 H H H H H M H
CO4 H H H H M H
H = Highly Related; M = Medium ; L = Low
92
M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester I
MCO 003A Advanced Operating Systems 3-0-0
Course Objective:
• To introduce the state of the art in operating systems and distributed systems,
• Learn how to design modern operating systems.
• To understand how to engage in systems research in general and operating systems
research in particular.
• To investigate novel ideas in operating systems through a semester-long research project.
Module 1:
Operating System: Definition, Operating System as Resource Manager. Types
of Operating Systems: Simple Batch Processing, Multi-programmed Batch
Processing, Time Sharing, Personal Computer systems, Parallel, Distributed and
Real Time Operating Systems. Operating System Components, Services, Calls,
System Programs, Operating System Structure, Virtual Machines, System
Design and Implementation.
Module 2:
Process Management: Concepts, Scheduling, Operations, Co-operating
processes, Inter-process Communication. Threads: Thread usage, threads in User
Space, threads in Kernel, Hybrid Implementation, Scheduler Activation, Pop-up
threads, Multithreading.
CPU Scheduling: Basic Concepts, Scheduling Criteria, Algorithms, Multiple-
processor Scheduling, Real Time Scheduling, Algorithm Evaluation.
93
Module 3:
Process Synchronization: Critical Section Problem, Synchronization
Hardware, Semaphores, Classical Problem of synchronization, Critical Regions,
Monitors. Deadlock: Characteristics, Necessary Conditions, Prevention,
Avoidance, Detection and Recovery.
Memory Management: Logical and Physical Address Space, Swapping.
Contiguous Allocation: Singlepartitioned, Multi-partitioned. Non-contiguous
Allocation: Paging, Segmentation, and Segmentation with Paging. Virtual
Memory: Demand Paging, Page Replacement Algorithms, Allocation of Frames,
Thrashing, Demand Segmentation.
Module 4:
File and Directory System: File Concepts, Access Methods, Directory
Structure, Protection, File system Structure, Allocation Methods, Free Space
Management, Directory Implementation, Recovery. Secondary Storage
Management: Disk Structure, Dedicated, Shared, Virtual, Sequential Access
and Random Access Devices, Disk Scheduling, Disk Management, Swap-space
Management, Disk Reliability, Stable Storage Management.
Protection and Security: Threats, Intruders, Accidental Data Loss,
Cryptography, User authentication, Attacks from inside the system, Attacks from
outside the system, Protection Mechanism, Trusted Systems, Domain of
Protection, Access Matrix, Programs Threats, System Threats.
Module 5:
Distributed systems, topology network types, design strategies. Network
operating structure, distributed operating system, remote services, and design
issues. Distributed file system: naming and transparency, remote file access,
Stateful v/s Stateless Service, File Replication.
Distributed co-ordinations: Event Ordering, Mutual Exclusion, Atomicity,
Concurrency Control, Deadlock Handling, Election Algorithms, and Reaching
Agreement. Case studies of Unix and MS-DOS operating system.
Suggested Books
1. Silberschatz and Galvin, "Operating System Concepts", Addison-Wesley publishing, Co.,1999.
2. A. S. Tanenbaum, “Modern Operating Systems”, Pearson Education.
3. H.M. Dietel, “An Introduction to Operating System”, Pearson Education.
4. D. M. Dhamdhere, “Operating Systems – A Concept Based Approach”, Tata McGraw-Hill
5 M. Singhal, N. G. Shivaratri, “Advanced Concepts in Operating Systems”, Tata McGraw
-Hill.
6. William Stallings, “Operating Systems”, Pearson Education
Course Outcomes:
At the end of the course, the student should be able to:
94
CO1. Understand the state of the art in operating systems and distributed systems, and how to
design modern operating systems.
CO2. Understand how to engage in systems research in general and operating systems research
in particular.
CO3. Investigate novel ideas in operating systems through a semester-long research project.
MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES:
Course
Outcome
Program Outcome Program
Specific
Outcome
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 H H M L H M
CO2 H H M M H M H H H
CO3 H H H H H M H H H H
H = Highly Related; M = Medium L = Low
95
M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester I
MCO 014A Advanced Data Mining and Warehousing 3-0-0
Course Objective:
• To compare and contrast different conceptions of data mining
• To explain the role of finding associations in commercial market basket data.
• To characterize the kinds of patterns that can be discovered by association rule mining.
• To describe how to extend a relational system
• To find patterns using association rules.
UNIT 1:
Overview: Concept of data mining and warehousing, data warehouse roles and
structures, cost of warehousing data, roots of data mining, approaches to data
exploration and data mining, foundations of data mining, web warehousing, web
warehousing for business applications and consumers, introduction to knowledge
management, data warehouses and knowledge bases.
UNIT 2:
Data Warehouse: Theory of data warehousing, barriers to successful data
warehousing, bad data warehousing approaches, stores, warehouse and marts,
data warehouse architecture, metadata, metadata extraction, implementing the
data warehouse and data warehouse technologies.
UNIT 3:
Data Mining and Data Visualisation: Data mining, OLAP, techniques used to
mine the data,market basket analysis, current limitations and challenges to DM,
data visualization.
Designing and Building the Data Warehouse: The enterprise model approach
of data mining design, data warehouse project plan, analysis and design tools,
data warehouse architecture,specification and development.
UNIT 4:
Web-Based Query and Reporting: Delivering information over the web, query
and reporting tools and business value, architectural approaches to delivering
query capabilities over the web.
Web Based Statistical Analysis and Data Mining: Analytical tools, business
value from analytical tools, humble spreadsheet, determining the business value
that analytical tools will deliver, statistical products overview – statistical
analysis applications, correlation analysis,regression analysis, data discovery
tools overview, data discovery applications, comparison of the products,
architectural approaches for statistical and data discovery tools.
UNIT 5:
Search Engines and Facilities: Search engines and the web, search engine
architecture, variations in the way the search facilities work and variations in
indexing schemes.
Future of Data Mining and Data Warehousing: Future of data warehousing,
trends in data warehousing, future of data mining, using data mining to protect
privacy, trends affecting the future of data mining and future of data
visualization.
96
Text Books
1. Jiwei Han, MichelienKamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann
Publishers an Imprint of Elsevier, 2001.
Reference Books:
1. ArunK.Pujari, Data Mining Techniques, Universities Press (India) Limited, 2001.
2. George M. Marakas, Modern Data warehousing, Mining and Visualization: core concepts,
Printice Hall, First Edition,2002.
Course Outcomes:
At the end of the course, students should be able to:
CO1. Compare and contrast different conceptions of data mining as evidenced in both research
and application.
CO2. Explain the role of finding associations in commercial market basket data.
CO3. Characterize the kinds of patterns that can be discovered by association rule mining.
CO4. Describe how to extend a relational system to find patterns using association rules.
CO5. Evaluate methodological issues underlying the effective application of data mining.
MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES:
Course
Outcome
Program Outcome Program
Specific
Outcome
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 H H H H H H
CO2 H M M M
CO3 H H H H M H
CO4 H H M M H H M H
H = Highly Related; M = Medium L = Low
97
M.Tech. in Computer Science & Engineering (Artificial Intelligence)- Semester I
HS0001 Research Methodology & Technical
Communication
3-0-0
Course Objective:
• To gain insights into how scientific research is conducted.
• To help in critical review of literature and assessing the research trends, quality and
extension potential of research and equip students to undertake research.
• To learn and understand the basic statistics involved in data presentation.
• To identify the influencing factor or determinants of research parameters.
Module 1:
Research: Meaning & Purpose, Review of literature, Problem
definition/Formulation of research problem, Research proposal, Variables,
Hypothesis, types, construction of hypothesis
Module 2:
Classification of research: Quantitative research: Descriptive Research,
Experimental Research
Qualitative research: Observational studies, Historical research, Focus group
discussion, Case study method
Module 3: Sources of data collection: Primary and Secondary Data Collection, Sample and
Sampling technology, Non-probability and Probability Sampling.
Module 4:
Tools for data collection: Tests, Interview, Observation, Questionnaire/
Schedule, Characteristics of a good test, Statistics: Descriptive and Inferential
Statistics,Data Analysis, Report Writing, Results and References
Module 5:
Thesis Writing and Journal Publications: Writing thesis, Writing journal and
conference papers, IEEE and Harvard style of referencing, Effective
presentation, Copyrights, and Avoid plagiarism
Course Outcome:
CO1. Gain insights into how scientific research is conducted.
CO2. Help in critical review of literature and assessing the research trends, quality and
extension potential of research and equip students to undertake research.
CO3. Learn and understand the basic statistics involved in data presentation.
CO4. Identify the influencing factor or determinants of research parameters.
Course
Outcome
Program Outcome Program Specific Outcome
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1
CO2
98
CO3
CO4
CO5
H = Highly Related; M = Medium ; L = Low
M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester I
MCO 008A Advanced Topics in Algorithm Lab 0-0-2
List of Experiments
1. Write a Program to implement Efficient Matrix Multiplication
2. Write a Program to define the graphs and list all nodes and Links
3. Write a Program to implement the concept of BFS
4. Write a Program to implement the concept of DFS
5. Write a Program to implement the concept of B-tree
6. Write a Program to implement Dijkistra Algorithm
7. Write a Program to implement the concept of Binomial Heap
8. Write a program to find Greatest Common Divisor
9. Write a program using Chinese remainder theorem
10 Write program to solve linear equations
11 Write a program to solve Travelling Salesman problem
12 Write a program to implement Vertex cover problem
13 Write a program to implement all pair shortest path Algorithm
99
M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester I
MCO Advanced Technology Lab 0-0-2
M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester I
MCO Research Methodology Lab 0-0-2
100
M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester II
MCO Principles of Artificial Intelligence & Machine
Learning
3-0-0
Course Objective:
• Able to explain the basic principles of artificial intelligence
• Students can apply logic and structured concepts in knowledge representation and
discuss the applications of artificial intelligence
• To implement and analyze uninformed and informed Search Strategies
• To implement and apply various game playing algorithms to different problems
• Understand and represent various types of logics and their forms
• To Understand various Learning techniques
Module 1:
Introduction- What is intelligence? Foundations of artificial intelligence (AI), Task of
artificial intelligence, Techniques of artificial intelligence, Problem Solving
Formulating problems, problem types, states and operators, state space.
Knowledge Representation- Role of Knowledge, Declarative Knowledge, Procedural
Knowledge, Knowledge representation Techniques; conceptual graphs; structured
representations; frames, scripts; issues in knowledge representation
Module 2:
Uninformed & Informed Search Strategies- Breath First Search, Depth First Search,
Depth Limited Search, Heuristic Functions, Best First Search, Hill Climbing Algorithm,
Problems and solutions of Hill Climbing, Iterative Deepening (IDA), A* algorithm,
AO* Algorithm.
Module 3:
Game playing- Introduction, Types of games, Minimax game algorithm, Alpha Beta
cut-off procedure. Case Study of Games
Module 4:
Logics- Propositional logics, First Order Predicate Logics (FOPL), Syntax of First
Order Predicate Logics, Properties of Wff, Clausal Forms, Conversion to clausal forms.
101
Module 5:
Machine Learning- Definition of learning systems, Goals and applications of machine
learning. Aspects of developing a learning system- Training data, Concept
representation, Function approximation, Issues in machine learning. Types of machine
learning-Learning associations. Supervised learning - Classification and regression
trees, Support vector machines. Unsupervised learning - Clustering, Instance-based
learning , K-nearest neighbor, Locally weighted regression, Radial basis function,
Reinforcement learning
Text Books:
1. Stuart Russell and Peter Norvig. Artificial Intelligence – A Modern Approach, Pearson
Education Press, 2001.
2. Kevin Knight, Elaine Rich, B. Nair, Artificial Intelligence, McGraw Hill, 2008.
3. Tom M. Mitchell, “Machine Learning”, McGraw-Hill Education (INDIAN EDITION), 2013.
Reference Books:
1. George F. Luger, Artificial Intelligence, Pearson Education, 2001.
2. Nils J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kauffman, 2002.
Course Outcomes: Upon the end of this course, student will be :
CO1: Familiar with the basic principles of artificial intelligence
CO2: To implement and analyze Uninformed and Informed Search algorithms
CO3: Able to represent and apply various logics and structured concepts in knowledge
representation
CO4: To implement and apply various game playing algorithms to different problems
CO5: To understand various Learning techniques
COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC
OUTCOMES:
Course
Outcome
Program Outcome Program Specific Outcome
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO1
0
PO1
1
PO1
2
PSO1 PSO2 PSO3
CO1 H L L M H L
102
CO2 H H H M H H M H M
CO3 H M M H H L L M M H M M
CO4 H M M H H L L H M H H M
CO5 H M M H L M
H = Highly Related; M = Medium L = Low
M.Tech. in Computer Science & Engineering (Artificial Intelligence)- Semester II
MCO 082A Pattern Recognition 3-0-0
Course Objective
• Understand how to generate pattern and explain how to analyze pattern features
• Understand how to build classifiers using non parametric methods.
• Learn and compare principles of parametric and non parametric classification
• Implement pattern recognition and machine learning theories
• Able to apply the pattern recognition theories to applications of interest
Module 1:
Pattern Recognition Overview
Overview of Pattern Recognition- Relations of PR with other Systems, PR
Applications, Different Approaches to Pattern Recognition, Classification and
Description—Patterns and feature extraction with Examples—Training and
Learning in PR systems—Pattern recognition Approaches.
Module 2:
Statistical Pattern Recognition
Introduction to statistical Pattern Recognition, Gaussian Case and Class
Dependency, Discriminate Function, Examples, Classifier Performance,
Module 3:
Linear Discriminant Functions and Unsupervised Learning and Clustering
Introduction—Discrete and binary Classification problems—Techniques to
directly obtain linear Classifiers, Formulation of Unsupervised Learning
Problems—Clustering for unsupervised learning and classification.
103
Module 4:
Syntactic Pattern Recognition
Overview of Syntactic Pattern Recognition—Syntactic recognition via parsing
and other grammars, Graphical Approaches to syntactic pattern recognition,
Learning via grammatical inference.
Module 5:
Recognition of Syntactic Description
Recognition by Matching, Recognition by Parsing, CYK Parsing Algorithm,
Augmented Transition Nets in Parsing, Graph Based structure representation,
Structured Strategy to Compare Attributed Graphs
At the end of the course, the student should be able to:
References:
1. Robert Schalkoff, “Pattern Recognition: Statistical Structural and Neural Approaches”,
John wiley& sons , Inc,1992.
2. Earl Gose, Richard johnsonbaugh, Steve Jost, “Pattern Recognition andImage
Analysis”, Prentice Hall of India,.Pvt Ltd, New Delhi, 1996.
3. Duda R.O., P.E.Hart& D.G Stork, “ Pattern Classification”, 2nd Edition, J.Wiley Inc
2001.
4. Duda R.O.& Hart P.E., “Pattern Classification and Scene Analysis”, J.wiley Inc, 1973.
Course Outcomes:
CO1. Understand and explain the process of Pattern Recognition.
CO2. Apply probability theory to estimate classifier performance.
CO3. Describe and analyze the principles of parametric and non parametric classification
methods.
CO4. Compare pattern classifications and pattern recognition techniques.
CO5. Apply Pattern Recognition techniques to real world problems & Design systems
Course
Outcome
Program Outcome Program Specific
Outcome
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 H M H
104
CO2 M M M M M
CO3 H H H H H
CO4 H N M H M
CO5 H M M H H H
H = Highly Related; M = Medium L = Low
M.Tech. in Computer Science & Engineering (Artificial Intelligence)- Semester II
MCO 090A Knowledge Engineering and Expert Systems 3-0-0
Course Objective: At the end of the course, the student should be able to:
• To explain and describe the concepts central to the creation of knowledge bases and expert
systems.
• Knowledgeable about the tools and the processes used for the creation of an expert system.
• Student will know methods used to evaluate the performance of an expert system.
105
• Students will be able to examine properties of existing systems in a case-study manner,
comparing differing approaches and software tools
UNIT1 Introduction To Knowledge Engineering: The Human Expert And An Artificial,
Expert Knowledge Base And Inference Engine, Importance of Expert System, features
of Expert System, Knowledge Acquisition And Knowledge Representation,
Components of a Knowledge in Expert system
UNIT2 Knowledge Acquisition & Problem Solving process: Introduction, Knowledge Acquisition
and domain Expert, Selection of the domain, Selection of the Knowledge Engineers,
Meetings and Plans, Organization of Meetings ,Documentation, Multiple domain
Experts. Selecting the appropriate Problem, Rule Based Systems, and Heuristic
Classifications Constructive Problem Solving.
UNIT3 Design of Expert System: Introduction, Stages in the Developing Expert System,
Errors in Development stages, Software Engineering and Expert Systems, The Expert
System Life Cycle, Expert System Design Examples, Case Based Reasoning, Semantic
of Expert, Systems.
UNIT4 Inference Engine: Inference Engine, Insight of Inference Engine, Search Strategies,
Forward Chaining Algorithm, Algorithms for forward Chaining- Baseline Version,
Backward Chaining Algorithm, Algorithms for Backward Chaining-Baseline Version,
Mixed Modes of Chaining, Work sheets for Forward and Backward Chaining
UNIT 5 Software Tools: Overview of Expert System Tools, Expert System Shells, Multiple
Paradigm Environments, Abstract architectures, Potential Implementation Problems,
Selecting a Software Tool, Implementation Mechanism of tools, Black Board
Architecture, Reasoning under uncertainty and Truth Maintenance Systems, Case-study :
DENDRAL and MYCIN
Text Books:
1. Peter Jackson, “Introduction to Expert Systems”,3rd Edition, Pearson Education 2007.
2. Robert I. Levine, Diane E. Drang, Barry Edelson: “ AI and Expert Systems: a comprehensive
guide, C language”, 2nd edition, McGraw-Hill 1990.
3. Jean-Louis Ermine: “Expert Systems: Theory and Practice”, 4th printing, Prentice-Hall of
India , 2001.
4. Stuart Russell, Peter Norvig: “Artificial Intelligence: A Modern Approach”,2nd Edition,Pearson
Education, 2007.
5. Padhy N.P.: “Artificial Intelligence and Intelligent Systems”,4th impression , Oxford University
Press, 2007.
Course Outcome:
CO1. To get introduced to the basic knowledge representation in Expert system
CO2. Understand the Knowledge Acquisition & Problem Solving methods
106
CO3. Understand, analyze and evaluate the performance of an expert system.
CO4. Understand and identify various rules in inference engine
CO5. Identify ,apply and compare Expert system software tool to solve real life
problems
Course
Outcome
Program Outcome Program Specific Outcome
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 H H M H
CO2 H H H M M
CO3 H M M M M M H M M
CO4 H M M M M M M
CO5 H M M M M M M M H H
H = Highly Related; M = Medium; L = Low
107
M.Tech. in Computer Science & Engineering (Artificial Intelligence)- Semester II
Artificial Neural Network and Deep Learning 3-0-0
Course Objectives
• To understand the concepts of Artificial neural networks
• To explore in-depth deep neural architectures for learning and inference
• To evaluate the performance of neural architectures in comparison to other machine
learning method
• Familiar with the fundamental principles, theory and approaches for learning with deep
neural networks
• Discuss Convolution Neural Network models to Applications
UNIT1 Introduction to Artificial Neural Network : Biological Neuron, Idea of computational
units, McCulloch–Pitts unit and Thresholding logic, Linear Perceptron, Perceptron
Learning Algorithm, Linear separability, Convergence theorem for Perceptron Learning
Algorithm, Type of network architecture, Activation functions, Basic Learning rules
UNIT2 Feed forward Networks: Multilayer Neural Network, Gradient Descent learning, Back
propagation, Empirical Risk Minimization, regularization, Radial Basis Neural Network
bias-variance trade off, regularization - over fitting - inductive bias regularization - drop
out - generalization.
UNIT3 Recurrent neural networks: Back propagation through time, Long Short Term
Memory, Gated Recurrent Units, Bidirectional LSTMs, Bidirectional RNNs
UNIT4 Deep Neural Networks: Introduction, Difficulty of training deep neural networks,
Greedy layer wise training. • Generative models: Restrictive Boltzmann Machines
(RBMs), Introduction to MCMC and Gibbs Sampling, gradient computations in RBMs,
Deep Boltzmann Machines. • Convolutional Neural Networks: LeNet, AlexNet, ZF-Net,
VGGNet, GoogLeNet, ResNet, Visualizing Convolutional Neural Networks, Guided
Back propagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks. •
Auto Encoders • Deep Reinforcement Learnin
UNIT 5 Convolutional Neural Network: Basic structure of Convolutional Network, Case
studies: Alex net, VGGNet, GoogLeNet, Applications of CNN
Text Books
1. Simon Haykin, “Neural Networks, A Comprehensive Foundation”, 2nd Edition, Addison
Wesley Longman, 2001.
2. Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer, 2006
108
3. Charu C.Aggarwal “Neural Networks and Deep learning” Springer International Publishing,
2018
4. Satish Kumar, “Neural Networks, A Classroom Approach”, Tata McGraw -Hill, 2007.
Course Outcomes
CO1. Explain the basic concepts in Neural Networks and applications
CO2. Discuss feed forward networks and their training issues
CO3. Distinguish different types of ANN architectures
CO4. Apply fundamental principles, theory and approaches for learning with deep neural
networks
CO5. Discuss & Apply Convolution Neural Network models to Applications
Course
Outcome
Program Outcome Program Specific
Outcome
PO
1
PO
2
PO
3
PO
4
PO
5
PO
6
PO
7
PO
8
PO
9
PO10 PO11 PO12 PSO1 PSO2 PSO
3
CO1 M L M
CO2 M L M
CO3 H M M M H L M
CO4 H M M M M H H H
CO5 H M M M M H H H
H = Highly Related; M = Medium ; L = Low
109
M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester II
MCO 089A Pattern Recognition Lab 0-0-2
Course Objectives:
• To introduce the most important concepts, techniques, and algorithms • Assess and
understand the challenges behind the design of machine vision systems.
• Understand the general processes of image acquisition, storage, enhancement,
segmentation, representation, and description.
• Implement filtering and enhancement algorithms for monochrome as well as color
images.
Course Outcomes:
CO1. To implement efficient algorithms for nearest neighbour classification, Linear
Discriminate Function
CO2. Able to identify the strengths and weaknesses of different types of classifiers &
implement them on simple applications.
CO3. Validate and assess and implement different clustering techniques
CO4. Be able to combine various classifiers using fixed rules or trained combiners and boost
their performance
CO5. Understand the possibilities and limitations in implementation of pattern recognition
techniques to different applications
Course Contents: Exercises that must be done in this course are listed below:
110
Lab 1. Implement a function for extracting the colour histogram of an image.
Lab 2. Read all the images from the training set. For each image compute the colour histogram
with general bin size m and save it as a row in the feature matrix X. Save the
corresponding class label in the label vector y.
Lab 3. Implement the k-NN classifier for an unknown image and for a general K value. Evaluate
the classifier on the test set by calculating the confusion matrix and the overall accuracy.
Lab 4. Try out different values for the number of bins for the histogram and the parameter K to
see which feature attains the best performance. Convert the input image into Luv or HSV
color-space before histogram calculation.
Lab 5. Data visualization, central limit theorem, multivariate normal distribution, data whitening,
non-parametric
Lab 6. Implement Hierarchical clustering, k-means, fuzzy c-means
Lab 7. Implementation of Bayesian classifier, k-NN classifier
Lab 8. Linear regression, MMSE, MAP, MLE, quality measures
Lab 9. Apply various dimensionality reduction methods whether through feature selection or
feature extraction. Assess classifier complexity and regularization parameters
Lab 10. Combine various classifiers using fixed rules or trained combiners and boost their
performance using some test data set from real world
M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester II
Artificial Neural Network and Deep Learning
Lab
0-0-2
Course Objectives:
At the end of the course
• The students should be able to design and implement machine learning solutions
• Understand classification, regression, and clustering problems;
• Able to evaluate and interpret the results of the algorithms.
Course Outcomes:
CO1. Create a custom feed-forward network.
CO2. Design Constructing Layers and Setting Transfer Functions
CO3. Implement Discriminative Learning models: Logistic Regression, Perceptrons, Artificial
Neural Networks.
List of Experiments
Lab 1. Create a custom feed-forward network .It consists of the following sections:
Constructing Layers , Connecting Layers , Setting Transfer Functions, Weights and
Biases , Training Functions & Parameters , Performance Functions , Train Parameters
111
Lab 2. Write a program to plot various membership functions.
Lab 3. Generate AND, NOT function using McCulloch-Pitts neural net program.
Lab 4. Generate XOR function using McCulloch-Pitts neural net.
Lab 5. Write a program for Perceptron net for an AND function with bipolar inputs and targets
Lab 6. Write a program of Perceptron Training Algorithm
Lab 7. Write a program of Back Propagation Algorithm.
Lab 8. Implement ANN and compare , regularization, overfitting, underfitting and drop out
Lab 9. Implement Convolutional Neural Networks (CNNs) and overcome overfitting with
dropout.
Lab 10. Implement Convolutional Neural Networks (CNNs) for Object detection
M.Tech. in Computer Science & Engineering (Artificial Intelligence)- Semester III
Application of Artificial Intelligence in Industries 3-0-0
Course Objectives
• Able to apply the concept of Artificial intelligence in various sectors
• Familiarize with applications of Artificial intelligence in banking applications.
• Appreciate the various applications in Communication and Education Industry.
• Identify the applications in Health care and Government sectors.
• Recognize the applications in Manufacturing industry and Transportations.
112
Module 1:
AI in Banking : Use of AI in banking and finance, Fraud detection, , Risk
modeling and investment banks, Customer data management, Decreased
customer experience and loyalty, Personalized marketing, Role of machine
learning: Challenges of banking sector and securities, Widely used machine
learning algorithms in banking and security, Fraud prevention and detection
systems, Rule based and machine learning based approach in fraud detection,
Anomaly detection: Ways to expose suspicious transactions in banks, Advanced
fraud detection systems, Risk management systems, Current challenges and
opportunities: Banking and security domain.
Module 2:
AI in Communication, Media & Healthcare: Usage of AI in media and
entertainment industry, Machine learning techniques for customer sentiment
analysis, Real-time analytics in communication, Real time analytics and social
media, Recommendations engines. The most important applications of machine
learning in healthcare, Role of machine learning in drug discovery, Medical
image analysis, Why deep learning for medical image analysis and Predictive
medicine: Prognosis and diagnostics accuracy, Predictive medicine
Module 3:
AI in Education & Manufacturing: Advantages of AI in education, learning
analytics, Academic analytics, Action research, Educational data mining,
Personalized adaptive learning, Learning analytics process, Case study:
Application of ML in predicting students’ performance.
Applications in manufacturing industry, Deep learning for smart manufacturing,
Machine learning for quality control in manufacturing, Case study, Construction
of CNN, Experimental results, Efficiency of CNN for defect detection,
Comparative experiments, Machine learning for fault assessment, Machinery
failure prevention technology.
Module 4:
AI in Government Administration: Type of government problems appropriate
for AI applications, AI for citizen services use cases, Answering questions,
Routing requests, Translation, Drafting documents, Chat bots for communication
between citizen and government, Media richness theory, Chatbots in the public
sector, Case study, Data management services, Knowledge processing services,
Application services.
Module 5:
AI in Transportation & Energy Sector: Applications of ML and artificial
intelligence in transportation, Incident detection, Predictive models, Application
of AI in aviation and public transportation, Aviation, Shared mobility, Buses,
Intelligent urban mobility, Autonomous vehicles, Autonomous transportation,
Artificial intelligence use cases in logistics, Back office AI, Cognitive customs,
Predictive logistics, Predictive risk management, Seeing thinking and speaking
logistics operations, ML powered customer experience, Limitations of AI
techniques in transportation,
AI in Smart grid technologies, Key characteristics of smart grid, Machine
learning applications in smart grid, Machine learning techniques for renewable
energy generation, Forecasting etc Case studies
113
TEXT BOOK
1. David Beyer, Artificial Intelligence and Machine Learning in Industry,: O'Reilly Media,
Inc.,ISBN: 9781491959336
2. Doug Hudgeon, Richard Nichol, Machine Learning for Business , December 2019 , ISBN
9781617295836
3. Application of machine learning in industries (IBM ICE Publications).
4. Andreas François Vermeulen, “Industrial Machine Learning”, Apress, Berkeley,
CA,2020
Course Outcomes
CO1. Familiarize, compare and analyze the role of AI in banking applications
CO2. Analyze the applications in Media and Health care Industry
CO3. Appreciate the various applications in manufacturing industry and Education
sectors.
CO4. Identify the problems in public sectors and role of AI in the solutions
CO5. Recognize the applications and challenges in Transportation and Energy Sectors
Course
Outcome
Program Outcome Program Specific Outcome
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 M M H H H H
CO2 H H H M H H H H
CO3 H H H M H H H H
CO4 H H H H M M
CO5 H H H H M M
H = Highly Related; M = Medium; L = Low
114
Faculty of Engineering & Technology
Syllabi and Course Structure
M. Tech. in Data Analytics
(Computer Science)
Academic Programs
April, 2019
115
Faculty of Engineering & Technology
M.Tech. in Computer Science & Engineering (Data Analytics)
Course Structure
First Semester
First Semester
Sub Code Sub Name L T P C
MCO 056A Advanced Data Structure and Algorithm Design 4 0 0 4
MCO 007A Advance Data Communication network 4 0 0 4
MCO 003A Advanced Operating Systems 4 0 0 4
MCO 011A Cloud Computing
Elective I
4 0 0 4
MCO 014A Advance Topics in Data Mining
and warehousing
MCO 021A Digital Image Processing
MCO 016A Information system security
MCO 070A Advanced Data Structure and Algorithm Lab 0 0 2 2
MCO 036A Advance Technology lab 0 0 2 2
MCO 010A Seminar 0 0 2 2
TOTAL 16 0 6 22
116
Faculty of Engineering & Technology
M.Tech. in Computer Science & Engineering (Data Analytics)
Second Semester
SECOND SEMESTER
Sub Code Sub Name L T P C
MCO 071A Foundations of Data Science 4 0 0 4
MCO 072A Programming for Data Analytics 4 0 0 4
HS0001 Research Methodology & Technical
communication
3 0 0 3
MCO 073A Deep Learning
Elective II
4 0 0 4
MCO 074A Big Data Technology
MCO 075A Internet of Things
MCO 076A Foundations of Data Science Lab 0 0 2 2
Quantitative Techniques & Computer
Applications Lab
0 0 1 1
MCO 077A Programming for Data Analytics Lab 0 0 2 2
MCO 019A Project 0 0 2 2
TOTAL 15 0 7 22
117
Faculty of Engineering & Technology
M.Tech. in Computer Science & Engineering (Data Analytics)
Third Semester
THIRD SEMESTER
Sub Code Sub Name L T P C
MCO 078A Social Networking and Mining 4 0 0 4
MCO 079A Health Care Data Analytics 4 0 0 4
MCO 080A Decision Management System
Elective III
4 0 0 4
MCO 081A Risk Analytics
MCO 082A Pattern Recognition
MCO 083A Big Data Security
Elective IV
4 0 0 4
MCO 084A Web Intelligence
MCO 085A Storage System
MCO 029A Dissertation-I 0 0 0 12
TOTAL 8 0 0 28
Fourth Semester
FOURTH SEMESTER
MCO 030A Dissertation-II 0 0 0 28
TOTAL 0 0 0 28
118
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 007A Advanced Data Communication Network 4-0-0
Course Objective
13. To provide a good conceptual understanding of advance computer networking
14. To understand various models and their functions
15. To have an advance understanding of performance evaluation
16. To understand network economics
Module 1:
The Motivation for Internetworking; Need for Speed and Quality of Service;
History of Networking and Internet; TCP/IP and ATM Networks; Internet
Services; TCP Services; TCP format and connection management;
Encapsulation in IP; UDP Services, Format and Encapsulation in IP; IP Services;
Header format and addressing; Fragmentation and reassembly; classless and
subnet address extensions; sub netting and super netting; CIDR; IPv6;
Module 2:
Congestion Control and Quality of Service: Data traffic; Network performance;
Effects of Congestion; Congestion Control; Congestion control in TCP and
Frame Relay; Link-Level Flow and Error Control; TCP flow control; Quality of
Service: Flow Characteristics, Flow Classes; Techniques to improve QoS;
Traffic Engineering; Integrated Services;
Module 3:
High Speed Networks: Packet Switching Networks; Frame Relay Networks;
Asynchronous Transfer Mode (ATM); ATM protocol Architecture; ATM logical
connections; ATM cells; ATM Service categories; ATM Adaptation Layer;
Optical Networks: SONET networks; SONET architecture;
Wireless WANs: Cellular Telephony; Generations; Cellular Technologies in
different generations; Satellite Networks;
Module 4:
Internet Routing: Interior and Exterior gateway Routing Protocols; Routers and
core routers; RIP; OSPF; BGP; IDRP; Multicasting; IGMP; MOSPF; Routing in
Ad Hoc Networks; Routing in ATM: Private Network-Network Interface;
Module 5:
Error and Control Messages: ICMP; Error reporting vs Error Correction; ICMP
message format and Delivery; Types of messages;
Address Resolution (ARP); BOOTP; DHCP; Remote Logging; File Transfer and
Access; Network Management and SNMP; Comparison of SMTP and HTTP;
Proxy Server; The Socket Interface;
119
Outcomes:
At the end of the course, the student should be able to:
9. Provide a good conceptual understanding of advance computer networking
10. Understand various models and their functions
11. Advance understanding of performance evaluation
12. Understand network economics
Text Books:
8. William Stallings, “High-Speed Networks and Internets, Performance and Quality of
Service”, Pearson Education;
9. Douglas E. Comer, “Internetworking with TCP/IP Volume – I, Principles, Protocols, and
Architectures”, Fourth Edition, Pearson Education.
Reference Books:
17. B. Muthukumaran, “Introduction to High Performance Networks”, Vijay Nicole Imprints.
18. Wayne Tomasi, “Introduction to Data Communications and Networking”, Pearson
Education.
19. James F. Kurose, Keith W. Ross, “Computer Networking, A Top-Down Approach
Featuring the Internet”, Pearson Education.
20. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education.
21. Behrouz A. Forouzan, “Data Communications and Networking”, Fourth Edition, McGraw
Hill.
Mahbub Hassan, Raj Jain, “High Performance TCP/IP Networking, Concepts, Issues, and
Solutions”, Pearson Education.
120
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 003A Advanced Operating Systems 4-0-0
Course Objective:
• To introduce the state of the art in operating systems and distributed systems, and how to
design modern operating systems.
• To understand how to engage in systems research in general and operating systems
research in particular.
• To investigate novel ideas in operating sytems through a semester-long research project.
Module 1:
Operating System: Definition, Operating System as Resource Manager. Types
of Operating Systems: Simple Batch Processing, Multi-programmed Batch
Processing, Time Sharing, Personal Computer systems, Parallel, Distributed and
Real Time Operating Systems. Operating System Components, Services, Calls,
System Programs, Operating System Structure, Virtual Machines, System
Design and Implementation.
Module 2:
Process Management: Concepts, Scheduling, Operations, Co-operating
processes, Inter-process Communication. Threads: Thread usage, threads in User
Space, threads in Kernel, Hybrid Implementation, Scheduler Activation, Pop-up
threads, Multithreading.
CPU Scheduling: Basic Concepts, Scheduling Criteria, Algorithms, Multiple-
processor Scheduling, Real Time Scheduling, Algorithm Evaluation.
Module 3:
Process Synchronization: Critical Section Problem, Synchronization
Hardware, Semaphores, Classical Problem of synchronization, Critical Regions,
Monitors. Deadlock: Characteristics, Necessary Conditions, Prevention,
Avoidance, Detection and Recovery.
Memory Management: Logical and Physical Address Space, Swapping.
Contiguous Allocation: Singlepartitioned, Multi-partitioned. Non-contiguous
Allocation: Paging, Segmentation, and Segmentation with Paging. Virtual
Memory: Demand Paging, Page Replacement Algorithms, Allocation of Frames,
Thrashing, Demand Segmentation.
Module 4:
File and Directory System: File Concepts, Access Methods, Directory
Structure, Protection, File system Structure, Allocation Methods, Free Space
Management, Directory Implementation, Recovery. Secondary Storage
Management: Disk Structure, Dedicated, Shared, Virtual, Sequential Access
and Random Access Devices, Disk Scheduling, Disk Management, Swap-space
Management, Disk Reliability, Stable Storage Management.
Protection and Security: Threats, Intruders, Accidental Data Loss,
Cryptography, User authentication, Attacks from inside the system, Attacks from
outside the system, Protection Mechanism, Trusted Systems, Domain of
Protection, Access Matrix, Programs Threats, System Threats.
121
Module 5:
Distributed systems, topology network types, design strategies. Network
operating structure, distributed operating system, remote services, and design
issues. Distributed file system: naming and transparency, remote file access,
Stateful v/s Stateless Service, File Replication.
Distributed co-ordinations: Event Ordering, Mutual Exclusion, Atomicity,
Concurrency Control, Deadlock Handling, Election Algorithms, and Reaching
Agreement. Case studies of Unix and MS-DOS operating system.
Outcomes:
At the end of the course, the student should be able to:
7. Understand the state of the art in operating systems and distributed systems, and how to
design modern operating systems.
8. Understand how to engage in systems research in general and operating systems research
in particular.
9. Investigate novel ideas in operating ystems through a semester-long research project.
Suggested Books
1. Silberschatz and Galvin, "Operating System Concepts", Addison-Wesley publishing, Co.,1999.
2. A. S. Tanenbaum, “Modern Operating Systems”, Pearson Education.
3. H.M. Dietel, “An Introduction to Operating System”, Pearson Education.
4. D. M. Dhamdhere, “Operating Systems – A Concept Based Approach”, Tata McGraw-Hill
5 M. Singhal, N. G. Shivaratri, “Advanced Concepts in Operating Systems”, Tata McGraw
-Hill.
6. William Stallings, “Operating Systems”, Pearson Education
122
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 011A Cloud Computing: Course Outlines 4-0-0
Course Objective:
9. To familiarize the philosophy, power, practical use of cloud.
10. To introduce fundamental principles, technology, and techniques of CC
11. To Discuss common problems that can be best solved with/in cloud
12. To Eliminate misconceptions about cloud computing
Module 1:
Understanding cloud computing: Introduction to Cloud Computing - Benefits
and Drawbacks - Types of Cloud Service Development - Deployment models
Module 2:
Cloud Architecture Technology and Architectural Requirements: The
Business Case for Clouds - Hardware and Infrastructure – Accessing the cloud –
Cloud Storage – Standards- Software as a Service – Discovering Cloud Services
Development tools. Three Layered Architectural Requirement - Provider
Requirements
Module 3:
Service Centric Issues - Interoperability - QoS - Fault Tolerance - Data
Management Storage and Processing - Virtualization Management - Scalability
- Load Balancing - Cloud Deployment for Enterprises - User Requirement -
Comparative Analysis of Requirement.
Module 4:
Security Management in Cloud: Security Management Standards - Security
Management in the Cloud Availability Management - SaaS Availability
Management - PaaS Availability Management - IaaS Availability Management
- Access Control - Security Vulnerability, Patch, and Configuration Management
– Privacy in Cloud- The Key Privacy Concerns in the Cloud - Security in Cloud
Computing.
Module 5:
Virtualization: Objectives - Benefits - Virtualization Technologies - Data
Storage Virtualization – Storage Virtualization – Improving Availability using
Virtualization - Improving Performance using Virtualization- Improving
Capacity using Virtualization.
Outcomes:
At the end of the course, the student should be able to:
9. Understand the philosophy, power, practical use of cloud.
10. Present fundamental principles, technology, and techniques of CC
11. Discuss common problems that can be best solved with/in cloud
12. Eliminate misconceptions about cloud computing
Text books:
123
13. David S Linthicum, “Cloud Computing and SOA Convergence in your Enterprise A Step
by Step Guide”, Addison Wesley Information Technology Series.
14. Anthony T Velte, Toby J.Velte, Robert Elsenpeter, “Cloud computing A Practical
Approach “, Tata McGraw Hill Publication
15. Tim Mather, SubraKumaraswamy, ShahedLatif, “Cloud Security and Privacy –
16. An Enterprise Perspective on Risks and Compliance” , O’Reilly Publications, First Edition
17. Michael Miller, “Cloud Computing – Web-Based Applications that Change the Way You
Work and Collaborate Online”, Pearson Education, New Delhi, 2009.
18. Cloud Computing Specialist Certification Kit – Virtualization Study Guide.
124
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 014A Advance Topics in Data Mining and Warehousing 3-0-0
Course Objective:
• To compare and contrast different conceptions of data mining as evidenced in both research
and application.
• To explain the role of finding associations in commercial market basket data.
• To characterize the kinds of patterns that can be discovered by association rule mining.
• To describe how to extend a relational system to find patterns using association rules.
UNIT 1:
Overview: Concept of data mining and warehousing, data warehouse roles and
structures, cost of warehousing data, roots of data mining, approaches to data
exploration and data mining, foundations of data mining, web warehousing, web
warehousing for business applications and consumers, introduction to knowledge
management, data warehouses and knowledge bases.
UNIT 2:
Data Warehouse: Theory of data warehousing, barriers to successful data
warehousing, bad data warehousing approaches, stores, warehouse and marts,
data warehouse architecture,metadata, metadata extraction, implementing the
data warehouse and data warehouse technologies.
UNIT 3:
Data Mining and Data Visualisation: Data mining, OLAP, techniques used to
mine the data,market basket analysis, current limitations and challenges to DM,
data visualization.
Designing and Building the Data Warehouse: The enterprise model approach
of data mining design, data warehouse project plan, analysis and design tools,
data warehouse architecture,specification and development.
UNIT 4:
Web-Based Query and Reporting: Delivering information over the web, query
and reporting tools and business value, architectural approaches to delivering
query capabilities over the web.
Web Based Statistical Analysis and Data Mining: Analytical tools, business
value from analytical tools, humble spreadsheet, determining the business value
that analytical tools will deliver, statistical products overview – statistical
analysis applications, correlation analysis,regression analysis, data discovery
tools overview, data discovery applications, comparison of the products,
architectural approaches for statistical and data discovery tools.
UNIT 5:
Search Engines and Facilities: Search engines and the web, search engine
architecture, variations in the way the search facilities work and variations in
indexing schemes.
Future of Data Mining and Data Warehousing: Future of data warehousing,
trends in data warehousing, future of data mining, using data mining to protect
privacy, trends affecting the future of data mining and future of data
visualization.
Outcomes:
125
At the end of the course, students should be able to:
• Compare and contrast different conceptions of data mining as evidenced in both research and
application.
• Explain the role of finding associations in commercial market basket data.
• Characterize the kinds of patterns that can be discovered by association rule mining.
• Describe how to extend a relational system to find patterns using association rules.
• Evaluate methodological issues underlying the effective application of data mining.
Text Books
1. Jiwei Han, MichelienKamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann
Publishers an Imprint of Elsevier, 2001.
Reference Books:
1. ArunK.Pujari, Data Mining Techniques, Universities Press (India) Limited, 2001.
2. George M. Marakas, Modern Data warehousing, Mining and Visualization: core concepts,
Printice Hall, First Edition,2002.
126
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 021A Digital Image Processing 4-0-0
Course Objective
• To cover the basic theory and algorithms that are widely used in digital image processing
• To expose students to current technologies and issues that are specific to image
processing system
• To develop hands-on experience in using computers to process images
• To familiarize with MATLAB Image Processing Toolbox
UNIT 1:
Fundamentals Of Image Processing
Introduction, Elements of visual perception, Steps in Image Processing
Systems, Image Acquisition, Sampling and Quantization, Pixel Relationships,
Colour Fundamentals and Models,File Formats. Introduction to the
Mathematical tools.
UNIT 2:
Image Enhancement and Restoration
Spatial Domain Gray level Transformations Histogram Processing Spatial
Filtering, Smoothing and Sharpening. Frequency Domain: Filtering in
Frequency Domain, DFT, FFT, DCT, Smoothing and Sharpening filters,
Homomorphic Filtering., Noise models, Constrained and Unconstrained
restoration models.
UNIT 3:
Image Segmentation and Feature Analysis
Detection of Discontinuities, Edge Operators, Edge Linking and Boundary
Detection, Thresholding, Region Based Segmentation, Motion Segmentation,
Feature Analysis and Extraction.
UNIT 4:
Multi Resolution Analysis and Compressions
Multi Resolution Analysis: Image Pyramids – Multi resolution expansion –
Wavelet Transforms,
Fast Wavelet transforms, Wavelet Packets. Image Compression: Fundamentals,
Models, Elements of Information Theory, Error Free Compression, Lossy
Compression, Compression Standards JPEG/MPEG.
UNIT 5:
Applications of Image Processing: Representation and Description, Image
Recognition, Image Understanding, Image Classification, Video Motion
Analysis, Image Fusion, Steganography, Colour Image Processing. Outcomes:
127
At the end of the course, the student should be able to:
• Cover the basic theory and algorithms that are widely used in digital image processing
• Expose students to current technologies and issues that are specific to image processing
system
• Develop hands-on experience in using computers to process images
• Familiarize with MATLAB Image Processing Toolbox .
Text Books:
1. Digital Image Processing - Dr. S.Sridhar Oxford University Press
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 016A Information System Security 3-0-0
Course Objective:
• To perform a risk assessment of an information system.
• To identify the security requirements for an information system.
• To use available government information system security resources when designing systems.
UNIT 1:
Introduction to Securities: Introduction to security attacks, services and
mechanism, Classical encryption techniques substitution ciphers and
transposition ciphers, cryptanalysis, steganography, Stream and block ciphers.
Modern Block Ciphers: Block ciphers principles, Shannon’s theory of confusion
and diffusion, fiestal structure, Data encryption standard (DES), Strength of
DES, Idea of differential cryptanalysis, block cipher modes of operations, Triple
DES
UNIT 2:
Modular Arithmetic: Introduction to group, field, finite field of the form GF(p),
modular arithmetic, prime and relative prime numbers, Extended Euclidean
Algorithm, Advanced Encryption Standard (AES) encryption and decryption
Fermat’s and Euler’s theorem, Primality testing, Chinese Remainder theorem,
Discrete Logarithmic Problem, Principals of public key crypto systems, RSA
algorithm, security of RSA
UNIT 3:
Message Authentication Codes: Authentication requirements, authentication
functions, message authentication code, hash functions, birthday attacks, security
of hash functions, Securehash algorithm (SHA)
Digital Signatures: Digital Signatures, Elgamal Digital Signature Techniques,
Digital signature standards (DSS), proof of digital signature algorithm
128
UNIT 4:
Key Management and distribution: Symmetric key distribution, Diffie-
Hellman Key Exchange, Public key distribution, X.509 Certificates, Public key
Infrastructure.
Authentication Applications: Kerberos
Electronic mail security: pretty good privacy (PGP), S/MIME.
UNIT 5:
IP Security: Architecture, Authentication header, Encapsulating security
payloads, combining security associations, key management. Introduction to
Secure Socket Layer, Secure electronic, transaction (SET).
System Security: Introductory idea of Intrusion, Intrusion detection, Viruses and
related threats,firewalls.
Outcomes:
At the end of the course, students should be able to:
• Perform a risk assessment of an information system.
• Identify the security requirements for an information system.
• Use available government information system security resources when designing systems.
Suggested Books:
1. William Stallings, “Cryptography and Network Security: Principals and Practice”,Pearson
Education.
2. Behrouz A. Frouzan: Cryptography and Network Security, TMH
3. Bruce Schiener, “Applied Cryptography”. John Wiley & Sons
4. Bernard Menezes,” Network Security and Cryptography”, Cengage Learning.
5. AtulKahate, “Cryptography and Network Security”, TMH
M.Tech. in Computer Science & Engineering (Data Analytics) Semester I
MCO 008A Advanced Topics in Algorithm Lab 0-0-2
List of Experiments
1. Write a Program to implement Efficient Matrix Multiplication
2. Write a Program to define the graphs and list all nodes and Links
3. Write a Program to implement the concept of BFS
4. Write a Program to implement the concept of DFS
5. Write a Program to implement the concept of B-tree
6. Write a Program to implement Dijkistra Algorithm
7. Write a Program to implement the concept of Binomial Heap
8. Write a program to find Greatest Common Divisor
129
9. Write a program using Chinese remainder theorem
10 Write program to solve linear equations
11 Write a program to solve Travelling Salesman problem
12 Write a program to implement Vertex cover problem
13 Write a program to implement all pair shortest path Algorithm
130
MCO 036A Advance Technology lab 0-0-2
The aim of this lab is to introduce the different simulation tools to the students. So that students
get familiar with different simulation environment and implement their theoretical knowledge.
22. Introduction of network Simulator.
23. Experiment Based on Network Simulator.
24. Introduction of OmNet .
25. Experiment Based on OmNet.
26. Introduction of WeKa.
27. Experiment Based on Weka.
28. Introduction based on SimSE.
29. Experiment Based on SimSE.
131
M.Tech. in Computer Science & Engineering (Data Analytics) Semester II
MCO 071A Foundation of Data Science 4-0-0
Course Objective
Module 1:
INTRODUCTION TO DATA SCIENCE
Data science process – roles, stages in data science project – working with data from files – working
with relational databases – exploring data – managing data – cleaning and sampling for modeling
and validation – introduction to NoSQL.
Module 2:
MODELING METHODS
Choosing and evaluating models – mapping problems to machine learning, evaluating clustering
models, validating models – cluster analysis – K-means algorithm, Naïve Bayes – Memorization
Methods – Linear and logistic regression – unsupervised methods.
Module 3:
INTRODUCTION TO R
Reading and getting data into R – ordered and unordered factors – arrays and matrices – lists and
data frames – reading data from files – probability distributions – statistical models in R -
manipulating objects – data distribution.
Module 4:
MAP REDUCE
Introduction – distributed file system – algorithms using map reduce, Matrix-Vector Multiplication
by Map Reduce – Hadoop - Understanding the Map Reduce architecture - Writing Hadoop
MapReduce Programs - Loading data into HDFS - Executing the Map phase - Shuffling and sorting
- Reducing phase execution.
Module 5:
DELIVERING RESULTS
Documentation and deployment – producing effective presentations – Introduction to graphical
analysis – plot() function – displaying multivariate data – matrix plots – multiple plots in one window
- exporting graph - using graphics parameters. Case studies.
At the end of the course, the student should be able to:
References:
1. Nina Zumel, John Mount, “Practical Data Science with R”, Manning Publications, 2014.
2. Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, “Mining of Massive Datasets”, Cambridge
University Press, 2014.
3. Mark Gardener, “Beginning R - The Statistical Programming Language”, John Wiley & Sons, Inc.,
2012.
4. W. N. Venables, D. M. Smith and the R Core Team, “An Introduction to R”, 2013.
5. Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta, “Practical Data Science
Cookbook”, Packt Publishing Ltd., 2014.
6. Nathan Yau, “Visualize This: The FlowingData Guide to Design, Visualization, and Statistics”,
Wiley, 2011.
7. Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, Wiley, ISBN:
9788126551071, 2015.
8. http://www.johndcook.com/R_language_for_programmers.html
9. http://bigdatauniversity.com/
10. http://home.ubalt.edu/ntsbarsh/stat-data/topics.htm#rintroduction
132
M.Tech. in Computer Science & Engineering (Data Analytics) Semester II
MCO 072A Programming for Data Analytics 4-0-0
Course Objective
Module 1:
NETWORK PROGRAMMING & DISTRIBUTED OBJECTS
Connecting to a Server - Implementing Servers and Clients- Advanced Socket Programming –
InetAddress - URL Connections – RMI Programming.
Module 2:
CONNECTING TO DATABASE
The Design of JDBC - Basic Concepts - Executing Queries – Prepared Statements - Result Sets –
Metadata -Transactions.
Module 3:
JAVABEANS
The Bean - Writing Process - Using Beans to Build an Application - Bean Property Types – Property
Editors - Customizers.
Module 4:
STREAMS AND FILES
Streams – Text Input and Output – Reading and Writing Binary Data – Zip Archives – Object
Streams and Serialization – Memory Mapped Files.
Module 5:
PROGRAMMING MAP REDUCE
MapReduce program in Java – Map Reduce API – Programming Examples- Combiner Functions -
Distributed MapReduce Job.
At the end of the course, the student should be able to:
References:
1. White, “Hadoop: The Definitive Guide”, Third Edition - 2012 – O’Reilly – ISBN:
9789350237564.
2. Cay S. Horstmann, Gary Cornell, “Core Java™ 2: Volume II–Advanced Features”, PrenticeHall,
9th edition, ISBN: 978-0137081608.
3. Jean Dollimore, Tim Kindberg, George Coulouris, “Distributed Systems Concepts and Design”,
4th Edition, Jun 2005, Hardback, 944 pages, ISBN: 9780321263544.
4. Y. Daniel Liang, Introduction to Java Programming, Tenth Edition, Pearson, 2015.
133
M.Tech. in Computer Science & Engineering (Artificial Intelligence)- Semester II
HS0001 Research Methodology & Technical
Communication
3-0-0
Course Objective:
• To gain insights into how scientific research is conducted.
• To help in critical review of literature and assessing the research trends, quality and
extension potential of research and equip students to undertake research.
• To learn and understand the basic statistics involved in data presentation.
• To identify the influencing factor or determinants of research parameters.
Module 1:
Research: Meaning & Purpose, Review of literature, Problem
definition/Formulation of research problem, Research proposal, Variables,
Hypothesis, types, construction of hypothesis
Module 2:
Classification of research: Quantitative research: Descriptive Research,
Experimental Research
Qualitative research: Observational studies, Historical research, Focus group
discussion, Case study method,
Module 3:
Sources of data collection: Primary and Secondary Data Collection, Sample and
Sampling technology, Non-probability and Probability Sampling
Module 4:
Tools for data collection: Tests, Interview, Observation, Questionnaire/
Schedule,Characteristics of a good test, Statistics: Descriptive and Inferential
Statistics
Data Analysis, Report Writing, Results and References,
Module 5:
Thesis Writing and Journal Publications: Writing thesis, Writing journal and
conference papers, IEEE and Harvard style of referencing, Effective
presentation, Copyrights, and Avoid plagiarism
Outcome:
At the end of the course, the student should be able to:
• Gain insights into how scientific research is conducted.
• Help in critical review of literature and assessing the research trends, quality and
extension potential of research and equip students to undertake research.
• Learn and understand the basic statistics involved in data presentation.
Identify the influencing factor or determinants of research parameters.
134
M.Tech. in Computer Science & Engineering (Data Analytics) Semester II
MCO 073A Deep Learning 4-0-0
Course Objective
Module 1:
FUNDAMENTALS CONCEPTS OF MACHINE LEARNING
Historical Trends in Deep Learning-Machine Learning Basics: Learning Algorithms-Supervised
and Unsupervised Training, Linear Algebra for machine Learning, Testing, Cross-Validation,
Dimensionality reduction, Over/Under-fitting, Hyper parameters and validation sets, Estimators,
Bias, Variance, Regularization-Introduction to a simple DNN, Platform for deep learning, Deep
learning software libraries.
Module 2:
DEEP FEED FORWARD NETWORKS
Deep feed forward networks-Introduction- Learning XOR- Gradient-Based Learning- Various
Activation Functions, error functions- Architecture Design-differentiation algorithms-
Regularization for Deep learning-Early Stopping, Drop out.
Module 3:
CONVOLUTIONAL NEURAL NETWORKS AND SEQUENCE MODELING
Convolutional Networks: Convolutional operation- Motivation- Pooling- Normalization,
Applications in Computer Vision: Imagenet- Sequence Modeling: Recurrent Neural Networks-
Difficulty in Training RNN- Encoder-Decoder.
Module 4:
AUTO ENCODERS
Auto encoders - Auto encoders: under complete, regularized, stochastic, denoising, contractive,
applications – dimensionality reduction, classification, recommendation, Optimization for Deep
Learning: optimizers. RMS Prop for RNNs, SGD for CNNs
Module 5:
DEEP ARCHITECTURES IN VISION
Deep Architectures in Vision - Alexnet to ResNet, Transfer learning, Siamese Networks, Metric
Learning, Ranking/Triplet loss, RCNNs, CNN-RNN, Applications in captioning and video tasks,
3D CNNs
At the end of the course, the student should be able to:
References:
1. Ian Goodfellow, YoshuaBengio, Aaron Courville, “Deep Learning”, MIT Press, 2016 (available at http://www.deeplearningbook.org)
2. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012
3. Michael Nielsen, “Neural Networks and Deep Learning”, Online book, 2016 (http://neuralnetworksanddeeplearning.com/)
4. Li Deng, Dong Yu, “Deep Learning: Methods and Applications”, Foundations and Trends in Signal Processing.
5. Christopher and M. Bishop, “Pattern Recognition and Machine Learning”, Springer Science Business Media, 2006.
6. Jason Brownlee , “Deep Learning with Python” , ebook, 2016N. D. Lewis , “Deep Learning Step by Step with Python: A Very Gentle Introduction to Deep Neural Networks for Practical Data Science
135
M.Tech. in Computer Science & Engineering (Data Analytics) Semester II
MCO 074A Big Data Technology 4-0-0
Course Objective
Module 1:
INTRODUCTION TO BIG DATA
Introduction – distributed file system – Big Data and its importance, Four Vs, Drivers for Big data,
Big data analytics, Big data applications. Algorithms using map reduce, Matrix-Vector
Multiplication by Map Reduce.
Module 2:
INTRODUCTION HADOOP
Big Data – Apache Hadoop & Hadoop EcoSystem – Moving Data in and out of Hadoop –
Understanding inputs and outputs of MapReduce - Data Serialization.
Module 3:
HADOOP ARCHITECTURE
Hadoop Architecture, Hadoop Storage: HDFS, Common Hadoop Shell commands , Anatomy of
File Write and Read., NameNode, Secondary NameNode, and DataNode, Hadoop MapReduce
paradigm, Map and Reduce tasks, Job, Task trackers - Cluster Setup – SSH & Hadoop
Configuration – HDFS Administering –Monitoring & Maintenance.
Module 4:
HADOOP ECOSYSTEM AND YARN
Hadoop ecosystem components - Schedulers - Fair and Capacity, Hadoop 2.0 New Features-
NameNode High Availability, HDFS Federation, MRv2, YARN, Running MRv1 in YARN.
Module 5:
HIVE AND HIVEQL, HBASE
Hive Architecture and Installation, Comparison with Traditional Database, HiveQL - Querying
Data - Sorting And Aggregating, Map Reduce Scripts, Joins & Subqueries, HBase concepts-
Advanced Usage, Schema Design, Advance Indexing - PIG, Zookeeper - how it helps in monitoring
a cluster, HBase uses Zookeeper and how to Build Applications with Zookeeper.
At the end of the course, the student should be able to:
References:
1. Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, Wiley,
ISBN: 9788126551071, 2015.
2. Chris Eaton, Dirk deroos et al. , “Understanding Big data ”, McGraw Hill, 2012.
3. Tom White, “HADOOP: The definitive Guide” , O Reilly 2012
4. Vignesh Prajapati, “Big Data Analytics with R and Haoop”, Packet Publishing 2013.
5. Tom Plunkett, Brian Macdonald et al, “Oracle Big Data Handbook”, Oracle Press, 2014.
6. http://www.bigdatauniversity.com/
7. Jy Liebowitz, “Big Data and Business analytics”,CRC press, 2013
136
M.Tech. in Computer Science & Engineering (Data Analytics) Semester II
MCO 075A Internet of things 4-0-0
Course Objective
Module 1: Overview, technology of the internet of things, enchanted objects, Design principles for
connected devices, Privacy, Web thinking for connected devices
Module 2:
Writing Code: building a program and deploying to a device, writing to Actuators, Blinking Led,
Reading from Sensors, Light Switch, Voltage Reader, Device as HTTP Client, HTTP, Push
Versus Pull
Module 3:
Pachube, Netduino, Sending HTTP Requests—the Simple Way, Sending HTTP Requests—the
Efficient Way
Module 4:
HTTP: Device as HTTP Server, Relaying Messages to and from the Netduino, Request Handlers,
Web Html, Handling Sensor Requests, Handling Actuator Requests
Module 5:
Going Parallel: Multithreading, Parallel Blinker, prototyping online components, using an API,
from prototypes to reality, business models, ethics, privacy, disrupting control, crowd sourcing
At the end of the course, the student should be able to:
References:
7. Adrian McEwen and Hakim Cassimally, “Designing the Internet of Things”, John Wiley & Sons, 2013.
8. Cuno Pfister, “Getting Started with the Internet of Things: Connecting Sensors and Microcontrollers to
the Cloud”, Maker Media, 2011.
9. Rob Barton, Gonzalo Salgueiro, David Hanes, “IoT Fundamentals: Networking Technologies,
Protocols, and Use Cases for the Internet of Things”, Cisco Press, 2017.
10. RadomirMihajlovic, Muthu Ramachandran, Reinhold Behringer, PetarKocovic “Emerging Trends and
Applications of the Internet of Things”, IGI Global, 2017.
11. HwaiyuGeng, “Internet of Things and Data Analytics Handbook”, John Wiley & Sons, 2017.
12. Marco Schwartz, “Internet of Things with Arduino Cookbook”, Packt Publishing, 2016.
137
M.Tech. in Computer Science & Engineering (Data Analytics) Semester II
MCO 076A Foundations of Data Science Lab 0-0-2
Course Objective
List of Experiment
1. (i) Perform setting up and Installing Hadoop in its two operating modes:
• Pseudo distributed,
• Fully distributed.
(ii) Use web based tools to monitor your Hadoop setup.
2. (i) Implement the following file management tasks in Hadoop:
• Adding files and directories
• Retrieving files
• Deleting files
(ii) Benchmark and stress test an Apache Hadoop cluster
3. Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm.
• Find the number of occurrence of each word appearing in the input file(s)
• Performing a MapReduce Job for word search count (look for specific keywords in a file)
4. Stop word elimination problem:
• Input:
o A large textual file containing one sentence per line
o A small file containing a set of stop words (One stop word per line)
• Output:
o A textual file containing the same sentences of the large input file without the words appearing
in the small file.
138
M.Tech. in Computer Science & Engineering (Data Analytics) Semester II
MCO 077A Programming for Data Analytics Lab 0-0-2
Course Objective
List of Experiment
1. Write a Map Reduce program that determines weather data. Weather sensors collecting data every hour at
many locations across the globe gather large volume of log data, which is a good candidate for analysis
with MapReduce, since it is semi structured and record-oriented. Data available
at:https://github.com/tomwhite/hadoop- book/tree/master/input/ncdc/all.
• Find average, max and min temperature for each year in NCDC dataset?
• Filterthereadingsofasetbasedonvalueofthemeasurement,Outputtheline
ofinputfilesassociatedwithatemperaturevaluegreaterthan30.0andstoreit in a separatefile.
2. Purchases.txtDataset
• Instead of breaking the sales down by store, give us a sales breakdown by product category across all of
ourstores
o What is the value of total sales for the followingcategories?
▪ Toys
▪ ConsumerElectronics
• Find the monetary value for the highest individual sale for each separatestore
o Whatarethevaluesforthefollowingstores?
▪ Reno
▪ Toledo
▪ Chandler
• Find the total sales value across all the stores, and the total number ofsales.
3. InstallandRunPigthenwritePigLatinscriptstosort,group,join,project,andfilteryourdata.
4. Write a Pig Latin scripts for finding TF-IDF value for book dataset (A corpus of eBooks available at:
ProjectGutenberg)
5. Install and Run Hive then use Hive to create, alter, and drop databases, tables, views, functions, andindexes.
6. Install, Deploy & configure Apache Spark Cluster. Run apache spark applications using Scala.
7. Data analytics using Apache Spark on Amazon food dataset, find all the pairs of items frequently
reviewedtogether.
• Write a single Spark applicationthat:
o TransposestheoriginalAmazonfooddataset,obtainingaPairRDDofthetype:
<user_id> → <list of the product_ids reviewed by user_id>
o Countsthefrequenciesofallthepairsofproductsreviewedtogether;
o Writes on the output folder all the pairs of products that appear more than once and their
frequencies. The pairs of products must be sorted byfrequency.
139
M.Tech. in Computer Science & Engineering (Data Analytics) Semester III
MCO 078A Social Networking and Mining 4-0-0
Course Objective
Module 1:
INTRODUCTION
Overview: Social network data-Formal methods- Paths and Connectivity-Graphs to represent social
relations-Working with network data- Network Datasets-Strong and weak ties - Closure, Structural
Holes, and Social Capital.
Module 2:
SOCIAL INFLUENCE
Homophily: Mechanisms Underlying Homophily, Selection and Social
Influence,Affiliation,Tracking Link Formation in OnLine Data, Spatial Model of Segregation -
Positive and Negative Relationships - Structural Balance - Applications of Structural Balance,
Weaker Form of Structural Balance.
Module 3:
INFORMATION NETWORKS AND THE WORLD WIDE WEB
The Structure of the Web- World Wide Web- Information Networks, Hypertext, and Associative
Memory- Web as a Directed Graph, Bow-Tie Structure of the Web- Link Analysis and Web Search-
Searching the Web: Ranking,Link Analysis using Hubs and Authorities- Page Rank- Link Analysis
in Modern Web Search,Applications, Spectral Analysis, Random Walks, and Web Search.
Module 4:
SOCIAL NETWORK MINING
Clustering of Social Network graphs: Betweenness, Girvan newman algorithm-Discovery of
communities- Cliques and Bipartite graphs-Graph partitioning methods-Matrices-Eigen values-
Simrank.
Module 5:
NETWORK DYNAMICS
Cascading Behavior in Networks:Diffusion in Networks,Modeling Diffusion - Cascades and Cluster,
Thresholds, Extensions of the Basic Cascade Model - Six Degrees of Separation-Structure and
Randomness, Decentralized Search- Empirical Analysis and Generalized Models- Analysis of
Decentralized Search
At the end of the course, the student should be able to:
References:
1. Easley and Kleinberg, “Networks, Crowds, and Markets: Reasoning about a highly connected
world”, Cambridge Univ. Press, 2010.
2. Robert A. Hanneman and Mark Riddle, “Introduction to social network methods”, University
of California, 2005.
3. Jure Leskovec,StanfordUniv.AnandRajaraman,Milliway Labs, Jeffrey D. Ullman, “Mining of Massive
Datasets”, Cambridge University Press, 2 edition, 2014.
4. Wasserman, S., & Faust, K, “Social Network Analysis: Methods and Applications”,
Cambridge University Press; 1 edition, 1994.
5. Borgatti, S. P., Everett, M. G., & Johnson, J. C., “Analyzing social networks”,
SAGEPublications Ltd; 1 edition, 2013.
6. John Scott , “Social Network Analysis: A Handbook” , SAGE Publications Ltd; 2nd edition,
2000.
140
M.Tech. in Computer Science & Engineering (Data Analytics) Semester III
MCO 079A Health Care Data Analytics 4-0-0
Course Objective
Module 1:
Introduction: Introduction to Healthcare Data Analytics- Electronic Health Records–
Components of EHR- Coding Systems- Benefits of EHR- Barrier to Adopting EHR- Challenges-
Phenotyping Algorithms.
Module 2:
Analysis: Biomedical Image Analysis- Mining of Sensor Data in Healthcare- Biomedical Signal
Analysis- Genomic Data Analysis for Personalized Medicine.
Module 3:
Analytics: Natural Language Processing and Data Mining for Clinical Text- Mining the
Biomedical- Social Media Analytics for Healthcare.
Module 4:
Advanced Data Analytics: Advanced Data Analytics for Healthcare– Review of Clinical
Prediction Models- Temporal Data Mining for Healthcare Data- Visual Analytics for Healthcare-
Predictive Models for Integrating Clinical and Genomic Data- Information Retrieval for
Healthcare- Privacy-Preserving Data Publishing Methods in Healthcare.
Module 5:
Applications: Applications and Practical Systems for Healthcare– Data Analytics for
Pervasive Health- Fraud Detection in Healthcare- Data Analytics for Pharmaceutical
Discoveries- Clinical Decision Support Systems- Computer-Assisted Medical Image Analysis
Systems- Mobile Imaging and Analytics for Biomedical Data.
At the end of the course, the student should be able to:
• Understand the various algorithm design technique.
• Learn analysis techniques to analyze the algorithms.
• Understand the advanced data structures, intrinsic complexity analysis,problem settings
References:
1. Chandan K. Reddy and Charu C Aggarwal, “Healthcare data analytics”, Taylor & Francis,2015
2. Hui Yang and Eva K. Lee, “Healthcare Analytics: From Data to Knowledge to Healthcare
Improvement, Wiley,2016.
141
M.Tech. in Computer Science & Engineering (Data Analytics) Semester III
MCO 080A Decision Management System 4-0-0
Course Objective
Module 1:
PRINCIPLES OF DMS
Principles of Decision Management Systems - Begin with the Decision in Mind - Be Transparent
and Agile - Be Predictive, Not Reactive - Test, Learn, and Continuously Improve.
Module 2:
BUILDING DECISION MANAGEMENT SYSTEMS
Building Decision Management Systems - Discover and Model Decisions - Characteristics of
Suitable Decisions - A Decision Taxonomy - Finding Decisions - Documenting Decisions -
Prioritizing Decisions.
Module 3:
DESIGN AND IMPLEMENT DECISION SERVICES
Design and Implement Decision Services - Build Decision Services - Integrate Decision Services -
Best Practices for Decision Services Construction - Monitor and Improve Decisions - What Is
Decision Analysis? - Monitor Decisions - Determine the Appropriate Response - Develop New
Decision-Making Approaches - Confirm the Impact Is as Expected - Deploy the Change.
Module 4:
ENABLERS FOR DECISION MANAGEMENT SYSTEMS
Enablers for Decision Management Systems - People Enablers - The Three-Legged Stool - A
Decision Management Center of Excellence - Organizational Change - Process Enablers -
Managing a Decision Inventory - Adapting the Software Development Lifecycle - Decision Service
Integration Patterns - Moving to Fact-Based Decisioning - The OODA Loop - Technology Enablers.
Module 5:
BUSINESS RULES MANAGEMENT SYSTEMS
Business Rules Management Systems - Predictive AnalyticsWorkbenches - Optimization
Systems - Pre-Configured Decision Management Systems - Data Infrastructure - A Service-
Oriented Platform.
At the end of the course, the student should be able to:
References:
1. James Taylor, “Decision Management Systems-A Practical guide to using Business rules and Predictive
Analytics”, IBM Press, 2012. 2. Efraim Turban , Jay E. Aronson , Ting-Peng Liang, “Decision Support Systems & Intelligent
Systems”, 9th edition, Prentice Hall, 2010.
3. Alberto Cordoba, “Understanding the Predictive Analytics Lifecycle”, Wiley, 2014.
4. Eric Siegel, Thomas H. Davenport, “Predictive Analytics: The Power to Predict Who Will Click, Buy,
Lie, or Die”, Wiley, 2013. 5. George M Marakas, “Decision support Systems”, 2nd Edition, Pearson/Prentice Hall,2002 6. V.S. Janakiraman, K. Sarukesi, “Decision Support Systems”,PHI, ISBN8120314441,
9788120314443, 2004.
7. Efrem G Mallach, “Decision Support systems and Data warehouse Systems”, McGraw Hill, thirteenth
reprint, 2008.
142
M.Tech. in Computer Science & Engineering (Data Analytics) Semester III
MCO 081A Risk Analytics 4-0-0
Course Objective
Module 1:
INTRODUCTION
Risk – Definitionand Examples, Components and Factors; Understanding Risk Assessment, Risk
Mitigation and Risk Management; Risk Analytics- Definition and Objectives.
Module 2:
RISK ANALYTICS FOR BANKING DOMAIN
Introduction to Banking Sector; National and International laws; Credit Risk Analytics , Internal
capital Adequacy Assessment Process related Risk Analytics , Limit Management , Risk-Adjusted
Performance Management ,Fraud Risk; Case Studies
Module 3:
RISK ANALYTICS FOR INSURANCE DOMAIN
Introduction to Insurance Sector; Property & Causality Insurance Companies and Life Insurance
Companies; Using Analytics for Customer Acquisition and Retention; Detecting, Preventing and
Managing Fraud using Analytics; Case Studies
Module 4:
RISK ANALYTICS FOR HEALTHCARE DOMAIN
Introduction to Healthcare Sector;HIPAA,Four Enterprise Disciplines of Health Analytics, Health
Outcome Analysis, Health Value and Cost; Customer Insights, Actuary Services, Framework for
Customer Analytics; Risk Management
Module 5:
WORKFORCE ANALYTICS
Workforce Environment and Psychology, HR Analytics and Talent Management- Understanding
and Predicting Retention, BoostingEmployee Engagement, Sources of Hire and Quality of Hire,
Profiling High Performers
At the end of the course, the student should be able to:
References:
5. Clark Abrahams and Mingyuan Zhang, “Credit Risk Assessment: The New Lending System
forBorrowers, Lenders, and Investors”, ISBN 978-0-470-46168-6
6. Naeem Siddiqi, “Credit Risk Scorecards: Developing and Implementing Intelligent CreditScoring”,
ISBN 978-0-471-75451-0
7. Laura B. Madsen, “Data-Driven Healthcare: How Analytics and BI are Transforming theIndustry”,
M.S.ISBN 978-1-118-77221-8
8. Jason Burke, “Health Analytics: Gaining the Insights to Transform Health Care”, John Wiley Sons Inc.,
2013, ISBN: 978-1-118-38304-9
9. Jac Fitz-Enz , John R. Mattox II, “Predictive Analytics for Human Resources”,ISBN-13: 978-
8126552153.
10. James C. Sesil, “Applying Advanced Analytics to HR Management Decisions: Methods for Selection,
Developing Incentives, and Improving Collaboration”, ISBN-13: 978-0133064605
Weblink:
1. http://www.capgemini.com/resource-file
access/resource/pdf/Analytics__A_Powerful_Tool_for_the_Life_Insurance_Industry.pdf
143
M.Tech. in Computer Science & Engineering (Data Analytics) Semester III
MCO 082A Pattern Recognition 4-0-0
Course Objective
Module 1:
PATTERN RECOGNITION OVERVIEW
Pattern recognition, Classification and Description—Patterns and feature extraction
withExamples—Training and Learning in PR systems—Pattern recognition Approaches.
Module 2:
STATISTICAL PATTERN RECOGNITION
Introduction to statistical Pattern Recognition—supervised Learning using Parametric and Non
Parametric Approaches.
Module 3:
LINEAR DISCRIMINANT FUNCTIONS AND UNSUPERVISED LEARNINGAND
CLUSTERING
Introduction—Discrete and binary Classification problems—Techniques to directly obtain linear
Classifiers -- Formulation of Unsupervised Learning Problems—Clustering for unsupervised
learning and classification.
Module 4:
SYNTACTIC PATTERN RECOGNITION
Overview of Syntactic Pattern Recognition—Syntactic recognition via parsing and other
grammars– Graphical Approaches to syntactic pattern recognition—Learning via grammatical
inference.
Module 5:
NEURAL PATTERN RECOGNITION
Introduction to Neural networks—Feedforward Networks and training by Back Propagation—
Content Addressable Memory Approaches and Unsupervised Learning in Neural PR
At the end of the course, the student should be able to:
References:
11. Robert Schalkoff, “Pattern Recognition: Statistical Structural and NeuralApproaches”, John wiley&
sons , Inc,1992.
12. Earl Gose, Richard johnsonbaugh, Steve Jost, “Pattern Recognition andImage Analysis”, Prentice
Hall of India,.Pvt Ltd, New Delhi, 1996.
13. Duda R.O., P.E.Hart& D.G Stork, “ Pattern Classification”, 2nd Edition, J.Wiley Inc 2001.
14. Duda R.O.& Hart P.E., “Pattern Classification and Scene Analysis”, J.wiley Inc, 1973.
15. Bishop C.M., “Neural Networks for Pattern Recognition”, Oxford University Press, 1995.
144
M.Tech. in Computer Science & Engineering (Data Analytics) Semester III
MCO 083A Big Data Security 4-0-0
Course Objective
Module 1:
BIG DATA PRIVACY, ETHICS AND SECURITY
Privacy – Reidentification of Anonymous People – Why Big Data Privacy is self regulating? –
Ethics – Ownership – Ethical Guidelines – Big Data Security – Organizational Security.
Module 2:
SECURITY, COMPLIANCE, AUDITING, AND PROTECTION
Steps to secure big data – Classifying Data – Protecting – Big Data Compliance – Intellectual
Property Challenge – Research Questions in Cloud Security – Open Problems.
Module 3:
– HADOOP SECURITY DESIGN
Kerberos – Default Hadoop Model without security - Hadoop Kerberos Security Implementation
& Configuration.
Module 4:
HADOOP ECOSYSTEM SECURITY
Configuring Kerberos for Hadoop ecosystem components – Pig, Hive, Oozie, Flume, HBase,
Sqoop.
Module 5:
DATA SECURITY & EVENT LOGGING
Integrating Hadoop with Enterprise Security Systems - Securing Sensitive Data in Hadoop – SIEM
system – Setting up audit logging in hadoop cluster
At the end of the course, the student should be able to:
References:
1. Mark Van Rijmenam, “Think Bigger: Developing a Successful Big Data Strategy for Your Business”,
Amazon, 1 edition, 2014.
2. Frank Ohlhorst John Wiley & Sons, “Big Data Analytics: Turning Big Data into Big Money”,
John Wiley & Sons, 2013.
3. SherifSakr, “Large Scale and Big Data: Processing and Management”, CRC Press, 2014.
4. Sudeesh Narayanan, “Securing Hadoop”, Packt Publishing, 2013.
5. Ben Spivey, Joey Echeverria, “Hadoop Security Protecting Your Big Data Problem”, O’Reilly
Media, 2015.
6. Top Tips for Securing Big Data Environments: e-book
(http://www.ibmbigdatahub.com/whitepaper/top-tips-securing-big-data-environments-e-book)
7. http://www.dataguise.com/?q=securing-hadoop-discovering-and-securing-sensitive-data-hadoop-data-
stores
145
M.Tech. in Computer Science & Engineering (Data Analytics) Semester III
MCO 084A Web Intelligence 4-0-0
Course Objective
Module 1:
Web Analytics – Basics – Traditional Ways – Expectations – Data Collection – Click stream
Data – Weblogs – Beacons – JavaScript Tags – Packet Sniffing – Outcomes data – Competitive
data – Search Engine Data.
Module 2:
Qualitative Analysis – Customer Centricity – Site Visits – Surveys – Questionnaires – Website
Surveys – Post visits – Creating and Running- Benefits of surveys – Critical components of
successful strategy.
Module 3:
Web Analytic concepts – URLS – Cookies – Time on site – Page views – Understand standard
reports – Website content quality – Navigation reports (top pages, top destinations, site overlay).
– Search Analytics – Internal search, SEO and PPC – Measuring Email and Multichannel
Marketing - Competitive intelligence and Web 2.0 Analytics – Segmentation – Connectable
reports.
Module 4:
Google Analytics: Analytics - Cookies - Accounts vs Property - Tracking Code - Tracking
Unique Visitors - Demographics - Page Views & Bounce Rate Acquisitions - Custom Reporting.
Module 5:
Goals & Funnels – Filters - Ecommerce Tracking - Real Time Reports - Customer Data Alert -
Adwords Linking - Adsense Linking -Attribution Modeling - Segmentation - Campaign Tracking
- Multi-Channel Attribution.
At the end of the course, the student should be able to:
References:
1. Avinash Kaushik, “Web Analytics 2.0: The Art of Online Accountability and Science Of Customer
Centricity “, 1st edition, Sybex, 2009.
2. Michael Beasley, “Practical Web Analytics for User Experience: How Analytics can help you
Understand your Users”, Morgan Kaufmann, 2013.
3. MagySeif El-Nasr, Anders Drachen, Alessandro Canossa, eds., “Game Analytics: Maximizing the Value
of Player Data”, Springer, 2013.
4. Bing Liu, “Web Data Mining: Exploring Hyperlinks, Content, and Usage Data”, 2nd Edition, Springer,
2011.
5. Justin Cutroni, “Google Analytics”, O’Reilly, 2010.
6. Eric Fettman, Shiraz Asif, FerasAlhlou , “Google Analytics Breakthrough”, John Wiley & sons, 2016.
146
M.Tech. in Computer Science & Engineering (Data Analytics) Semester III
MCO 085A Storage System 4-0-0
Course Objective
Module 1:
INTRODUCTION TO STORAGE AND MANAGEMENT
Introduction to Information Storage Management - Data Center Environment–Database
Management System (DBMS) - Host - Connectivity –Storage-Disk Drive Components- Intelligent
Storage System -Components of an Intelligent Storage System- Storage Provisioning- Types of
Intelligent Storage Systems.
Module 2:
STORAGE NETWORKING
Fiber Channel: Overview - SAN and Its Evolution -Components of FC SAN -FC Connectivity-FC
Architecture- IPSAN-FCOE-FCIP-Network-Attached Storage- General-Purpose Servers versus
NAS Devices - Benefits of NAS- File Systems and Network File Sharing-Components of NAS -
NAS I/O Operation -NAS Implementations -NAS File-Sharing Protocols-Object-Based Storage
Devices-Content-Addressed Storage -CAS UseCases.
Module 3:
BACKUP AND RECOVERY
Business Continuity -Information Availability -BC Terminology-BC Planning Life Cycle - Failure
Analysis -Business Impact Analysis-Backup and Archive - Backup Purpose -Backup
Considerations -Backup Granularity - Recovery Considerations -Backup Methods -Backup
Architecture - Backup and Restore Operations.
Module 4:
CLOUD COMPUTING
Cloud Enabling Technologies -Characteristics of Cloud Computing -Benefits of Cloud Computing
- Cloud Service Models-Cloud Deployment models-Cloud computing Infrastructure-Cloud
Challenges.
Module 5:
SECURING AND MANAGING STORAGE INFRASTRUCTURE
Information Security Framework -Storage Security Domains-Security Implementations in Storage
Networking - Monitoring the Storage Infrastructure -Storage Infrastructure Management Activities
- Storage Infrastructure Management Challenges.
At the end of the course, the student should be able to:
References:
1. EMC Corporation, Information Storage and Management, WileyIndia, 2nd Edition, 2011. 2. Robert Spalding, “Storage Networks: The Complete Reference”, Tata McGraw Hill, Osborne, 2003. 3. Marc Farley, Building Storage Networks, Tata McGraw Hill , Osborne,2nd Edition, 2001.
Meeta Gupta, Storage Area Network Fundamentals, Pearson Education Limited, 2002.
147
School of Engineering & Technology
Syllabi and Course Structure
M. Tech. in Computer Science and
Engineering
(Spl. Cyber Security)
Academic Programmes
April, 2019
148
The main objective of this program is to train students to become cyber security professionals
for the high-end jobs in the security industry. The objective of this programme is to create
security professionals who will be handling the real-life problems and challenges the industry
is facing today in connection to cyber security.
The unique design of the Programme focuses on providing a high degree of industry exposure,
academic and functional experts from the industry in this domain.
This programme offers a brilliant career pathway to those who are passionate about knowing
more about security challenges and solutions as well as practicing as:
149
School of Engineering & Technology
M.Tech. in Computer Science & Engineering (Cyber Security)
Course Structure
First Semester
First Semester
Sub Code Sub Name L T P C
MCO 056A Advanced Data Structure and Algorithm Design 4 0 0 4
MCO 007A Advance Data Communication network 4 0 0 4
MCO 003A Advanced Operating Systems 4 0 0 4
MCO 011A Cloud Computing
Elective I
4 0 0
MCO 014A Advance Topics in Data Mining
and warehousing
MCO 021A Digital Image Processing
MCO 016A Information system security
MCO 070A Advanced Data Structure and Algorithm Lab 0 0 2 2
MCO 036A Advance Technology lab 0 0 2 2
MCO 010A Seminar 0 0 2 2
TOTAL 16 0 06 22
150
School of Engineering & Technology
M.Tech. inComputer Science & Engineering (Cyber Security)
Second Semester
SECOND SEMESTER
Sub Name L T P C
MCO 057A Ethical Hacking 4 0 0 4
MCO 058A Cyber Laws & Security Policies 4 0 0 4
HS 0001 Research Methodology & Technical
Communication
3 0 0 3
MCO 013A Secure Ecommerce
Elective II
4 0 0 4
MCO 059A Security Threats &
Vulnerabilities
MCO 060A Wireless and Mobile
Security
Quantitative Techniques & Computer
Applications Lab
0 0 1 1
MCO 061A Ethical hacking and Digital Forensic Tools
Lab
0 0 2 2
MCO 062 A Cryptography And Security Lab 0 0 2 2
MCO 019A Project 0 0 2 2
TOTAL 15 0 07 22
151
School of Engineering & Technology
M.Tech. inComputer Science & Engineering (Cyber Security)
Third Semester
THIRD SEMESTER
Sub Code Sub Name L T P C
MCO 063A Secure Protocol Design 4 0 0 4
MCO 064A Digital Forensics
4 0 0 4
MCO 065A Intrusion Detection Systems
Elective III
4 0 0 4
MCO 066A Database and Application
Security
MCO 067A Data Science for Security
Analysis
MCO 068A Vulnerability Discovery and
Exploit Development
Elective IV
4 0 0 4
MCO 026A Biometric Security
MCO 069A Information Security Risk
Management
MCO 028A PKI and Trust Management
MCO 029A Dissertation-I 0 0 0 12
TOTAL 16 0 0 28
Fourth Semester
FOURTH SEMESTER
MCO 030A Dissertation-II 0 0 0 28
TOTAL 0 0 0 28
152
M.Tech. in Computer Science & Engineering (Cyber Security) Semester I
MCO 007A Advanced Data Communication Network 4-0-0
Course Objective
17. To provide a good conceptual understanding of advance computer networking
18. To understand various models and their functions
19. To have an advance understanding of performance evaluation
20. To understand network economics
Module 1:
The Motivation for Internetworking; Need for Speed and Quality of Service;
History of Networking and Internet; TCP/IP and ATM Networks; Internet
Services; TCP Services; TCP format and connection management;
Encapsulation in IP; UDP Services, Format and Encapsulation in IP; IP Services;
Header format and addressing; Fragmentation and reassembly; classless and
subnet address extensions; sub netting and super netting; CIDR; IPv6;
Module 2:
Congestion Control and Quality of Service: Data traffic; Network performance;
Effects of Congestion; Congestion Control; Congestion control in TCP and
Frame Relay; Link-Level Flow and Error Control; TCP flow control; Quality of
Service: Flow Characteristics, Flow Classes; Techniques to improve QoS;
Traffic Engineering; Integrated Services;
Module 3:
High Speed Networks: Packet Switching Networks; Frame Relay Networks;
Asynchronous Transfer Mode (ATM); ATM protocol Architecture; ATM logical
connections; ATM cells; ATM Service categories; ATM Adaptation Layer;
Optical Networks: SONET networks; SONET architecture;
Wireless WANs: Cellular Telephony; Generations; Cellular Technologies in
different generations; Satellite Networks;
Module 4:
Internet Routing: Interior and Exterior gateway Routing Protocols; Routers and
core routers; RIP; OSPF; BGP; IDRP; Multicasting; IGMP; MOSPF; Routing in
Ad Hoc Networks; Routing in ATM: Private Network-Network Interface;
Module 5:
Error and Control Messages: ICMP; Error reporting vs Error Correction; ICMP
message format and Delivery; Types of messages;
Address Resolution (ARP); BOOTP; DHCP; Remote Logging; File Transfer and
Access; Network Management and SNMP; Comparison of SMTP and HTTP;
Proxy Server; The Socket Interface;
Outcomes:
At the end of the course, the student should be able to:
153
13. Provide a good conceptual understanding of advance computer networking
14. Understand various models and their functions
15. Advance understanding of performance evaluation
16. Understand network economics
Text Books:
10. William Stallings, “High-Speed Networks and Internets, Performance and Quality of
Service”, Pearson Education;
11. Douglas E. Comer, “Internetworking with TCP/IP Volume – I, Principles, Protocols, and
Architectures”, Fourth Edition, Pearson Education.
Reference Books:
22. B. Muthukumaran, “Introduction to High Performance Networks”, Vijay Nicole Imprints.
23. Wayne Tomasi, “Introduction to Data Communications and Networking”, Pearson
Education.
24. James F. Kurose, Keith W. Ross, “Computer Networking, A Top-Down Approach
Featuring the Internet”, Pearson Education.
25. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education.
26. Behrouz A. Forouzan, “Data Communications and Networking”, Fourth Edition, McGraw
Hill.
Mahbub Hassan, Raj Jain, “High Performance TCP/IP Networking, Concepts, Issues, and
Solutions”, Pearson Education.
154
M.Tech. in Computer Science & Engineering (Cyber Security) Semester I
MCO 003A Advanced Operating Systems 4-0-0
Course Objective:
• To introduce the state of the art in operating systems and distributed systems, and how to
design modern operating systems.
• To understand how to engage in systems research in general and operating systems
research in particular.
• To investigate novel ideas in operating sytems through a semester-long research project.
Module 1:
Operating System: Definition, Operating System as Resource Manager. Types
of Operating Systems: Simple Batch Processing, Multi-programmed Batch
Processing, Time Sharing, Personal Computer systems, Parallel, Distributed and
Real Time Operating Systems. Operating System Components, Services, Calls,
System Programs, Operating System Structure, Virtual Machines, System
Design and Implementation.
Module 2:
Process Management: Concepts, Scheduling, Operations, Co-operating
processes, Inter-process Communication. Threads: Thread usage, threads in User
Space, threads in Kernel, Hybrid Implementation, Scheduler Activation, Pop-up
threads, Multithreading.
CPU Scheduling: Basic Concepts, Scheduling Criteria, Algorithms, Multiple-
processor Scheduling, Real Time Scheduling, Algorithm Evaluation.
Module 3:
Process Synchronization: Critical Section Problem, Synchronization
Hardware, Semaphores, Classical Problem of synchronization, Critical Regions,
Monitors. Deadlock: Characteristics, Necessary Conditions, Prevention,
Avoidance, Detection and Recovery.
Memory Management: Logical and Physical Address Space, Swapping.
Contiguous Allocation: Singlepartitioned, Multi-partitioned. Non-contiguous
Allocation: Paging, Segmentation, and Segmentation with Paging. Virtual
Memory: Demand Paging, Page Replacement Algorithms, Allocation of Frames,
Thrashing, Demand Segmentation.
Module 4:
File and Directory System: File Concepts, Access Methods, Directory
Structure, Protection, File system Structure, Allocation Methods, Free Space
Management, Directory Implementation, Recovery. Secondary Storage
Management: Disk Structure, Dedicated, Shared, Virtual, Sequential Access
and Random Access Devices, Disk Scheduling, Disk Management, Swap-space
Management, Disk Reliability, Stable Storage Management.
Protection and Security: Threats, Intruders, Accidental Data Loss,
Cryptography, User authentication, Attacks from inside the system, Attacks from
outside the system, Protection Mechanism, Trusted Systems, Domain of
Protection, Access Matrix, Programs Threats, System Threats.
155
Module 5:
Distributed systems, topology network types, design strategies. Network
operating structure, distributed operating system, remote services, and design
issues. Distributed file system: naming and transparency, remote file access,
Stateful v/s Stateless Service, File Replication.
Distributed co-ordinations: Event Ordering, Mutual Exclusion, Atomicity,
Concurrency Control, Deadlock Handling, Election Algorithms, and Reaching
Agreement. Case studies of Unix and MS-DOS operating system.
Outcomes:
At the end of the course, the student should be able to:
10. Understand the state of the art in operating systems and distributed systems, and how to
design modern operating systems.
11. Understand how to engage in systems research in general and operating systems research
in particular.
12. Investigate novel ideas in operating ystems through a semester-long research project.
Suggested Books
1. Silberschatz and Galvin, "Operating System Concepts", Addison-Wesley publishing, Co.,1999.
2. A. S. Tanenbaum, “Modern Operating Systems”, Pearson Education.
3. H.M. Dietel, “An Introduction to Operating System”, Pearson Education.
4. D. M. Dhamdhere, “Operating Systems – A Concept Based Approach”, Tata McGraw-Hill
5 M. Singhal, N. G. Shivaratri, “Advanced Concepts in Operating Systems”, Tata McGraw
-Hill.
6. William Stallings, “Operating Systems”, Pearson Education
156
M.Tech. in Computer Science & Engineering (Cyber Security) Semester I
MCO 011A Cloud Computing: Course Outlines 4-0-0
Course Objective:
13. To familiarize the philosophy, power, practical use of cloud.
14. To introduce fundamental principles, technology, and techniques of CC
15. To Discuss common problems that can be best solved with/in cloud
16. To Eliminate misconceptions about cloud computing
Module 1:
Understanding cloud computing: Introduction to Cloud Computing - Benefits
and Drawbacks - Types of Cloud Service Development - Deployment models
Module 2:
Cloud Architecture Technology and Architectural Requirements: The
Business Case for Clouds - Hardware and Infrastructure – Accessing the cloud –
Cloud Storage – Standards- Software as a Service – Discovering Cloud Services
Development tools. Three Layered Architectural Requirement - Provider
Requirements
Module 3:
Service Centric Issues - Interoperability - QoS - Fault Tolerance - Data
Management Storage and Processing - Virtualization Management - Scalability
- Load Balancing - Cloud Deployment for Enterprises - User Requirement -
Comparative Analysis of Requirement.
Module 4:
Security Management in Cloud: Security Management Standards - Security
Management in the Cloud Availability Management - SaaS Availability
Management - PaaS Availability Management - IaaS Availability Management
- Access Control - Security Vulnerability, Patch, and Configuration Management
– Privacy in Cloud- The Key Privacy Concerns in the Cloud - Security in Cloud
Computing.
Module 5:
Virtualization: Objectives - Benefits - Virtualization Technologies - Data
Storage Virtualization – Storage Virtualization – Improving Availability using
Virtualization - Improving Performance using Virtualization- Improving
Capacity using Virtualization.
Outcomes:
At the end of the course, the student should be able to:
13. Understand the philosophy, power, practical use of cloud.
14. Present fundamental principles, technology, and techniques of CC
15. Discuss common problems that can be best solved with/in cloud
16. Eliminate misconceptions about cloud computing
Text books:
157
19. David S Linthicum, “Cloud Computing and SOA Convergence in your Enterprise A Step
by Step Guide”, Addison Wesley Information Technology Series.
20. Anthony T Velte, Toby J.Velte, Robert Elsenpeter, “Cloud computing A Practical
Approach “, Tata McGraw Hill Publication
21. Tim Mather, SubraKumaraswamy, ShahedLatif, “Cloud Security and Privacy –
22. An Enterprise Perspective on Risks and Compliance” , O’Reilly Publications, First Edition
23. Michael Miller, “Cloud Computing – Web-Based Applications that Change the Way You
Work and Collaborate Online”, Pearson Education, New Delhi, 2009.
24. Cloud Computing Specialist Certification Kit – Virtualization Study Guide.
158
M.Tech. in Computer Science & Engineering (Cyber Security) Semester I
MCO 014A Advance Topics in Data Mining and Warehousing 3-0-0
Course Objective:
• To compare and contrast different conceptions of data mining as evidenced in both research
and application.
• To explain the role of finding associations in commercial market basket data.
• To characterize the kinds of patterns that can be discovered by association rule mining.
• To describe how to extend a relational system to find patterns using association rules.
UNIT 1:
Overview: Concept of data mining and warehousing, data warehouse roles and
structures, cost of warehousing data, roots of data mining, approaches to data
exploration and data mining, foundations of data mining, web warehousing, web
warehousing for business applications and consumers, introduction to knowledge
management, data warehouses and knowledge bases.
UNIT 2:
Data Warehouse: Theory of data warehousing, barriers to successful data
warehousing, bad data warehousing approaches, stores, warehouse and marts,
data warehouse architecture,metadata, metadata extraction, implementing the
data warehouse and data warehouse technologies.
UNIT 3:
Data Mining and Data Visualisation: Data mining, OLAP, techniques used to
mine the data,market basket analysis, current limitations and challenges to DM,
data visualization.
Designing and Building the Data Warehouse: The enterprise model approach
of data mining design, data warehouse project plan, analysis and design tools,
data warehouse architecture,specification and development.
UNIT 4:
Web-Based Query and Reporting: Delivering information over the web, query
and reporting tools and business value, architectural approaches to delivering
query capabilities over the web.
Web Based Statistical Analysis and Data Mining: Analytical tools, business
value from analytical tools, humble spreadsheet, determining the business value
that analytical tools will deliver, statistical products overview – statistical
analysis applications, correlation analysis,regression analysis, data discovery
tools overview, data discovery applications, comparison of the products,
architectural approaches for statistical and data discovery tools.
UNIT 5:
Search Engines and Facilities: Search engines and the web, search engine
architecture, variations in the way the search facilities work and variations in
indexing schemes.
Future of Data Mining and Data Warehousing: Future of data warehousing,
trends in data warehousing, future of data mining, using data mining to protect
privacy, trends affecting the future of data mining and future of data
visualization.
Outcomes:
159
At the end of the course, students should be able to:
• Compare and contrast different conceptions of data mining as evidenced in both research and
application.
• Explain the role of finding associations in commercial market basket data.
• Characterize the kinds of patterns that can be discovered by association rule mining.
• Describe how to extend a relational system to find patterns using association rules.
• Evaluate methodological issues underlying the effective application of data mining.
Text Books
1. Jiwei Han, MichelienKamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann
Publishers an Imprint of Elsevier, 2001.
Reference Books:
1. ArunK.Pujari, Data Mining Techniques, Universities Press (India) Limited, 2001.
2. George M. Marakas, Modern Data warehousing, Mining and Visualization: core concepts,
Printice Hall, First Edition,2002.
160
M.Tech. in Computer Science & Engineering (Cyber Security) Semester I
MCO 021A Digital Image Processing 4-0-0
Course Objective
• To cover the basic theory and algorithms that are widely used in digital image processing
• To expose students to current technologies and issues that are specific to image
processing system
• To develop hands-on experience in using computers to process images
• To familiarize with MATLAB Image Processing Toolbox
UNIT 1:
Fundamentals Of Image Processing
Introduction, Elements of visual perception, Steps in Image Processing
Systems, Image Acquisition, Sampling and Quantization, Pixel Relationships,
Colour Fundamentals and Models,File Formats. Introduction to the
Mathematical tools.
UNIT 2:
Image Enhancement and Restoration
Spatial Domain Gray level Transformations Histogram Processing Spatial
Filtering, Smoothing and Sharpening. Frequency Domain: Filtering in
Frequency Domain, DFT, FFT, DCT, Smoothing and Sharpening filters,
Homomorphic Filtering., Noise models, Constrained and Unconstrained
restoration models.
UNIT 3:
Image Segmentation and Feature Analysis
Detection of Discontinuities, Edge Operators, Edge Linking and Boundary
Detection, Thresholding, Region Based Segmentation, Motion Segmentation,
Feature Analysis and Extraction.
UNIT 4:
Multi Resolution Analysis and Compressions
Multi Resolution Analysis: Image Pyramids – Multi resolution expansion –
Wavelet Transforms,
Fast Wavelet transforms, Wavelet Packets. Image Compression: Fundamentals,
Models, Elements of Information Theory, Error Free Compression, Lossy
Compression, Compression Standards JPEG/MPEG.
UNIT 5:
Applications of Image Processing: Representation and Description, Image
Recognition, Image Understanding, Image Classification, Video Motion
Analysis, Image Fusion, Steganography, Colour Image Processing. Outcomes:
At the end of the course, the student should be able to:
• Cover the basic theory and algorithms that are widely used in digital image processing
• Expose students to current technologies and issues that are specific to image processing
system
• Develop hands-on experience in using computers to process images
• Familiarize with MATLAB Image Processing Toolbox .
Text Books:
161
1. Digital Image Processing - Dr. S.Sridhar Oxford University Press
M.Tech. in Computer Science & Engineering (Cyber Security) Semester I
MCO 016A Information System Security 3-0-0
Course Objective:
• To perform a risk assessment of an information system.
• To identify the security requirements for an information system.
• To use available government information system security resources when designing systems.
UNIT 1:
Introduction to Securities: Introduction to security attacks, services and
mechanism, Classical encryption techniques substitution ciphers and
transposition ciphers, cryptanalysis, steganography, Stream and block ciphers.
Modern Block Ciphers: Block ciphers principles, Shannon’s theory of confusion
and diffusion, fiestal structure, Data encryption standard (DES), Strength of
DES, Idea of differential cryptanalysis, block cipher modes of operations, Triple
DES
UNIT 2:
Modular Arithmetic: Introduction to group, field, finite field of the form GF(p),
modular arithmetic, prime and relative prime numbers, Extended Euclidean
Algorithm, Advanced Encryption Standard (AES) encryption and decryption
Fermat’s and Euler’s theorem, Primality testing, Chinese Remainder theorem,
Discrete Logarithmic Problem, Principals of public key crypto systems, RSA
algorithm, security of RSA
UNIT 3:
Message Authentication Codes: Authentication requirements, authentication
functions, message authentication code, hash functions, birthday attacks, security
of hash functions, Securehash algorithm (SHA)
Digital Signatures: Digital Signatures, Elgamal Digital Signature Techniques,
Digital signature standards (DSS), proof of digital signature algorithm
UNIT 4:
Key Management and distribution: Symmetric key distribution, Diffie-
Hellman Key Exchange, Public key distribution, X.509 Certificates, Public key
Infrastructure.
Authentication Applications: Kerberos
Electronic mail security: pretty good privacy (PGP), S/MIME.
UNIT 5:
IP Security: Architecture, Authentication header, Encapsulating security
payloads, combining security associations, key management. Introduction to
Secure Socket Layer, Secure electronic, transaction (SET).
System Security: Introductory idea of Intrusion, Intrusion detection, Viruses and
related threats,firewalls.
Outcomes:
At the end of the course, students should be able to:
• Perform a risk assessment of an information system.
• Identify the security requirements for an information system.
162
• Use available government information system security resources when designing systems.
Suggested Books:
1. William Stallings, “Cryptography and Network Security: Principals and Practice”,Pearson
Education.
2. Behrouz A. Frouzan: Cryptography and Network Security, TMH
3. Bruce Schiener, “Applied Cryptography”. John Wiley & Sons
4. Bernard Menezes,” Network Security and Cryptography”, Cengage Learning.
5. AtulKahate, “Cryptography and Network Security”, TMH
163
M.Tech. in Computer Science & Engineering (Cyber Security) Semester I
MCO 008A Advanced Topics in Algorithm Lab 0-0-2
List of Experiments
1. Write a Program to implement Efficient Matrix Multiplication
2. Write a Program to define the graphs and list all nodes and Links
3. Write a Program to implement the concept of BFS
4. Write a Program to implement the concept of DFS
5. Write a Program to implement the concept of B-tree
6. Write a Program to implement Dijkistra Algorithm
7. Write a Program to implement the concept of Binomial Heap
8. Write a program to find Greatest Common Divisor
9. Write a program using Chinese remainder theorem
10 Write program to solve linear equations
11 Write a program to solve Travelling Salesman problem
12 Write a program to implement Vertex cover problem
13 Write a program to implement all pair shortest path Algorithm
164
MCO 036A Advance Technology lab 0-0-2
The aim of this lab is to introduce the different simulation tools to the students. So that students
get familiar with different simulation environment and implement their theoretical knowledge.
30. Introduction of network Simulator.
31. Experiment Based on Network Simulator.
32. Introduction of OmNet .
33. Experiment Based on OmNet.
34. Introduction of WeKa.
35. Experiment Based on Weka.
36. Introduction based on SimSE.
165
M.Tech. in Computer Science & Engineering (Cyber Security) Semester II
MCO 057A Ethical Hacking 4-0-0
Course Objective:
The main objective of this course is to render every database based transaction safe, secure and
simple. We aim to transform the internet security industry by infusing professionalism and a
never before seen efficiency.
Module 1:
Hacking Windows: BIOS Passwords, Windows Login Passwords, Changing
Windows Visuals, Cleaning Your Tracks, Internet Explorer Users, Cookies,
URL Address Bar, Netscape Communicator, Cookies, URL History, The
Registry, Baby Sitter Programs.
Module 2:
Advanced Windows Hacking: Editing your Operating Systems by editing
Explorer.exe, The Registry, The Registry Editor, Description of .reg file,
Command Line Registry Arguments, Other System Files, Some Windows &
DOS Tricks, Customize DOS, Clearing the CMOS without opening your PC,
The Untold Windows Tips and Tricks Manual, Exiting Windows the Cool and
Quick Way, Ban Shutdowns: A Trick to Play, Disabling Display of Drives in My
Computer, Take Over the Screen Saver, Pop a Banner each time Windows Boots,
Change the Default Locations, Secure your Desktop Icons and Settings.
Module 3:
Getting Past the Password: Passwords: An Introduction, Password Cracking,
Cracking the Windows Login Password, The Glide Code, Windows Screen
Saver Password, XOR, Internet Connection Password, Sam Attacks, Cracking
Unix Password Files, HTTP Basic Authentication, BIOS Passwords, Cracking
Other Passwords, .
Module 4:
The Perl Manual: Perl: The Basics, Scalars, Interacting with User by getting
Input, Chomp() and Chop(), Operators, Binary Arithmetic Operators, The
Exponentiation Operator(**), The Unary Arithmetic Operators, Other General
Operators, Conditional Statements, Assignment Operators. The?: Operator,
Loops, The While Loop, The For Loop, Arrays, THE FOR EACH LOOP:
Moving through an Array, Functions Associated with Arrays, Push() and Pop(),
Unshift() and Shift(), Splice(), Default Variables, $_, @ARGV, Input Output,
Opening Files for Reading, Another Special VariableS.
Module 5:
How does a Virus Work? What is a Virus?, Boot Sector Viruses (MBR or
Master Boot Record), File or Program Viruses, Multipartite Viruses, Stealth
Viruses, Polymorphic Viruses, Macro Viruses, Blocking Direct Disk Access,
Recognizing Master Boot Record (MBR) Modifications, Identifying Unknown
Device Drivers, How do I make my own Virus?, Macro Viruses, Using
Assembly to Create your own Virus, How to Modify a Virus so Scan won‟t
Catch it, How to Create New Virus Strains, Simple Encryption Methods.
At the end of the course, the student should be able to:
1. Learn various hacking methods.
2. Perform system security vulnerability testing.
166
3. Perform system vulnerability exploit attacks.
4. Produce a security assessment report
5. Learn various issues related to hacking
TEXT BOOKS:
1. Patrick Engbreston: “The Basics of Hacking and Penetration Testing: Ethical Hacking and
Penetration Testing Made Easy”,1st Edition, Syngress publication,2011.
2. Ankit Fadia : “Unofficial Guide to Ethical Hacking”, 3rd Edition , McMillan India Ltd,2006.
REFERENCES:
1. Simpson/backman/corley, “HandsOn Ethical Hacking & Network Defense International”, 2nd
Edition,Cengageint,2011.
167
M.Tech. in Computer Science & Engineering (Cyber Security) Semester II
MCO 058A Cyber Laws And Security Policies 4-0-0
Course Objective
The Objectives Of This Course Is To Enable Learner To Understand, Explore, And Acquire A
Critical Understanding Cyber Law. Develop Competencies For Dealing With Frauds And
Deceptions (Confidence Tricks, Scams) And Other Cyber Crimes For Example, Child
Pornography Etc. That Are Taking Place Via The Internet.
Module 1:
Introduction to Cyber Law Evolution of Computer Technology : Emergence
of Cyber space. Cyber Jurisprudence, Jurisprudence and law, Doctrinal
approach, Consensual approach, Real Approach, Cyber Ethics, Cyber
Jurisdiction, Hierarchy of courts, Civil and criminal jurisdictions, Cyberspace-
Web space, Web hosting and web Development agreement, Legal and
Technological Significance of domain Names, Internet as a tool for global
access.
Module 2:
Information technology Act : Overview of IT Act, 2000, Amendments and
Limitations of IT Act, Digital Signatures, Cryptographic Algorithm, Public
Cryptography, Private Cryptography, Electronic Governance, Legal
Recognition of Electronic Records, Legal Recognition of Digital Signature
Certifying Authorities, Cyber Crime and Offences, Network Service Providers
Liability, Cyber Regulations Appellate Tribunal, Penalties and Adjudication.
Module 3:
Introduction to Cyber Law Evolution of Computer Technology : Emergence
of Cyber space. Cyber Jurisprudence, Jurisprudence and law, Doctrinal
approach, Consensual approach, Real Approach, Cyber Ethics, Cyber
Jurisdiction, Hierarchy of courts, Civil and criminal jurisdictions, Cyberspace-
Web space, Web hosting and web Development agreement, Legal and
Technological Significance of domain Names, Internet as a tool for global
access.
Module 4:
Electronic Business and legal issues: Evolution and development in E-
commerce, paper vs paper less contracts E-Commerce models- B2B, B2C,E
security.
Application area: Business, taxation, electronic payments, supply chain, EDI,
E-markets, Emerging Trends.
Module 5:
Case Study On Cyber Crimes: Harassment Via E-Mails, Email Spoofing
(Online A Method Of Sending E-Mail Using A False Name Or E-Mail Address
To Make It Appear That The E-Mail Comes From Somebody Other Than The
True Sender, Cyber Pornography (Exm.MMS),Cyber-Stalking
At the end of the course, the student should be able to:
1. Make Learner Conversant With The Social And Intellectual Property Issues Emerging From
Cyberspace.
168
2. Explore The Legal And Policy Developments In Various Countries To Regulate Cyberspace;
3. Develop The Understanding Of Relationship Between Commerce And Cyberspace; And
4. Give Learners In Depth Knowledge Of Information Technology Act And Legal Frame Work
Of Right To Privacy, Data Security And Data Protection.
5. Make Study On Various Case Studies On Real Time Crimes.
TEXT BOOKS :
1 .K.Kumar,” Cyber Laws: Intellectual property & E Commerce, Security”,1st Edition,
Dominant Publisher,2011.
2. Rodney D. Ryder, “ Guide To Cyber Laws”, Second Edition, Wadhwa And Company, New
Delhi, 2007.
3. Information Security policy &implementation Issues, NIIT, PHI.
REFERENCES :
1. Vakul Sharma, "Handbook Of Cyber Laws" Macmillan India Ltd, 2nd Edition,PHI,2003.
2. Justice Yatindra Singh, " Cyber Laws", Universal Law Publishing, 1st Edition,New Delhi,
2003.
3. Sharma, S.R., “Dimensions Of Cyber Crime”, Annual Publications Pvt. Ltd., 1st Edition,
2004.
4. Augastine, Paul T.,” Cyber Crimes And Legal Issues”, Crecent Publishing Corporation, 2007.
169
HS0001 Research Methodology & Technical
Communication
3-0-0
Course Objective:
• To gain insights into how scientific research is conducted.
• To help in critical review of literature and assessing the research trends, quality and
extension potential of research and equip students to undertake research.
• To learn and understand the basic statistics involved in data presentation.
• To identify the influencing factor or determinants of research parameters.
Module 1:
Research: Meaning & Purpose, Review of literature, Problem
definition/Formulation of research problem, Research proposal, Variables,
Hypothesis, types, construction of hypothesis
Module 2:
Classification of research: Quantitative research: Descriptive Research,
Experimental Research
Qualitative research: Observational studies, Historical research, Focus group
discussion, Case study method,
Module 3:
Sources of data collection: Primary and Secondary Data Collection, Sample and
Sampling technology, Non-probability and Probability Sampling
Module 4:
Tools for data collection: Tests, Interview, Observation, Questionnaire/
Schedule,Characteristics of a good test, Statistics: Descriptive and Inferential
Statistics
Data Analysis, Report Writing, Results and References,
Module 5:
Thesis Writing and Journal Publications: Writing thesis, Writing journal and
conference papers, IEEE and Harvard style of referencing, Effective
presentation, Copyrights, and Avoid plagiarism
Outcome:
At the end of the course, the student should be able to:
• Gain insights into how scientific research is conducted.
• Help in critical review of literature and assessing the research trends, quality and
extension potential of research and equip students to undertake research.
• Learn and understand the basic statistics involved in data presentation.
Identify the influencing factor or determinants of research parameters.
170
M.Tech. in Computer Science & Engineering (Cyber Security) Semester II
MCO 013A Secure E-Commerce 3-0-0
Course Objective:
• Understand the importance and scope of the security of information systems for EC.
• Describe the major concepts and terminology of EC security.
• Learn about the major EC security threats, vulnerabilities, and risks.
• Understand phishing and its relationship to financial crimes.
• Describe the information assurance security principles.
UNIT 1:
The importance of e-commerce security to the business enterprise. Current
threats facing organizations that conduct business online and how to mitigate
these challenges.
UNIT 2:
Cryptography review public key certificates and infrastructures, authentication
and authorization certificates secure credential services and role-based
authorization
UNIT 3:
Mobile code security, security of agent-based systems
UNIT 4:
Secure electronic transactions, electronic payment systems
UNIT 5:
Intellectual property protection, Law and Regulation
Outcomes:
At the end of the course, students should be able to:
• Understand the importance and scope of the security of information systems for EC.
• Describe the major concepts and terminology of EC security.
• Learn about the major EC security threats, vulnerabilities, and risks.
• Understand phishing and its relationship to financial crimes.
• Describe the information assurance security principles
Text Books:
1. Gary Schneider, Electronic Commerce, Sixth Edition, Course Technologies,2006, ISBN: 0-619-
21704-9
Reference Books:
1.Awad, E., Electronic Commerce: From Vision to Fulfillment, 3/E, Prentice Hall, 2006, ISBN:
0-13-1735217
2. Davis, W., Benamati, J., E-Commerce Basics: Technology Foundations and E-Business
Applications, Prentice Hall, 2003, ISBN: 0-201-74840-1
171
3. Ford, W., Baum, M., Secure Electronic Commerce: Building the Infrastructure for Digital
Signatures and Encryption, 2/E, Prentice Hall, 2001, ISBN: 0-13-027276-0
4. Mostafa Hashem Sherif, Protocols for Secure Electronic Commerce, Second Edition, CRC
Press, 20043 ISBN: 0849315093.
6. National Institutes of Standards and Technology (NIST) Special Publications
172
M.Tech. in Computer Science & Engineering (Cyber Security) Semester II
MCO 059A SECURITY THREATS & VULNERABILITIES 4-0-0
Course Objective:
The main objective of this course is that to provide security to various systems by identifying
various Types of threats and vulnerabilities.
Module 1:
Introduction: Security threats - Sources of security threats- Motives - Target
Assets and Vulnerabilities. Consequences of threats- E-mail threats - Web-
threats - Intruders and Hackers,Insider threats, Cyber crimes.
Module 2:
Network Threats: Active/ Passive – Interference – Interception – Impersonation
– Worms – Virus – Spam‟s – Ad ware - Spy ware – Trojans and covert channels
– Backdoors – Bots - IP Spoofing - ARP spoofing - Session Hijacking -
Sabotage-Internal treats- Environmental threats - Threats to Server security.
Module 3:
Security Threat Management: Risk Assessment - Forensic Analysis - Security
threat correlation – Threat awareness - Vulnerability sources and assessment-
Vulnerability assessment tools - Threat identification - Threat Analysis - Threat
Modeling - Model for Information Security Planning.
Module 4:
Security Elements: Authorization and Authentication - types, policies and
techniques - Security certification - Security monitoring and Auditing - Security
Requirements Specifications - Security Policies and Procedures, Firewalls, IDS,
Log Files, Honey Pots
Module 5:
Access control, Trusted Computing and multilevel security - Security models,
Trusted Systems, Software security issues, Physical and infrastructure security,
Human factors – Security awareness, training, Email and Internet use policies.
At the end of the course, the student should be able to:
1. The Student will gain the knowledge on various security threats and issues and how to
overcome those issues.
2. The student will get the capability to handle various attackers and crime issues.
3. Learning various issues involved in threats overcome methods.
4. Learning Forensic analysis and risk analysis.
5. Learn inner security issues involved in mail agents, viruses and worms.
TEXT BOOKS:
1. Swiderski, Frank and Syndex: “Threat Modeling”, 1st Edition, Microsoft Press, 2004.
2. Joseph M Kizza: “Computer Network Security”, 1st Edition, Springer, 2010.
3. William Stallings and Lawrie Brown: “Computer Security: Principles and Practice”, 2nd
Edition Prentice Hall, 2008.
REFERENCES:
1. Lawrence J Fennelly : “Handbook of Loss Prevention and Crime Prevention” 5th Edition,
Butterworth-Heinemann,2012.
173
2. Tipton Ruthbe Rg : “Handbook of Information Security Management”, 6th Edition, Auerbach
Publications,2010.
3. Mark Egan : “The Executive Guide to Information Security” , 1st Edition, Addison-Wesley
Professional,2004.
174
M.Tech. in Computer Science & Engineering (Cyber Security) Semester II
MCO 060A WIRELESS AND MOBILE SECURITY 4-0-0
Course Objective:
This skill oriented course equips the system Administrators with the skills required to protect &
recover the computer systems & networks from various security threats.
Module 1:
Security Issues in Mobile Communication: Mobile Communication History,
Security – Wired Vs Wireless, Security Issues in Wireless and Mobile
Communications, Security Requirements in Wireless and Mobile
Communications, Security for Mobile Applications, Advantages and
Disadvantages of Application – level Security.
Module 2:
Security of Device, Network, and Server Levels: Mobile Devices Security
Requirements, Mobile Wireless network level Security, Server Level Security.
Application Level Security in Wireless Networks: Application of WLANs,
Wireless Threats, Some Vulnerabilities and Attach Methods over WLANs,
Security for 1G Wi-Fi Applications, Security for 2G Wi-Fi Applications, Recent
Security Schemes for Wi-Fi Applications
Module 3:
Application Level Security in Cellular Networks: Generations of Cellular
Networks, Security Issues and attacks in cellular networks, GSM Security for
applications, GPRS Security for applications, UMTS security for applications,
3G security for applications, Some of Security and authentication Solutions.
Module 4:
Application Level Security in MANETs: MANETs, Some applications of
MANETs, MANET Features, Security Challenges in MANETs, Security
Attacks on MANETs, External Threats for MANET applications, Internal threats
for MANET Applications, Some of the Security Solutions.
Module 5:
Data Center Operations - Security challenge, implement “Five Principal
Characteristics of Cloud Computing, Data center Security Recommendations
Encryption for Confidentiality and Integrity, Encrypting data at rest, Key
Management Lifecycle, Cloud Encryption Standards.
At the end of the course, the student should be able to:
1. Familiarize with the issues and technologies involved in designing a wireless and mobile
system that is robust against various attacks.
2. Gain knowledge and understanding of the various ways in which wireless networks can be
attacked and tradeoffs in protecting networks.
3. Have a broad knowledge of the state-of-the-art and open problems in wireless and mobile
security, thus enhancing their potential to do research or pursue a career in this rapidly
developing area.
4. Learn various security issues involved in cloud computing.
5. Learn various security issues related to GPRS and 3G.
TEXT BOOKS:
175
1. Pallapa Venkataram, Satish Babu: “Wireless and Mobile Network Security”, 1st Edition, Tata
McGraw Hill,2010.
2. Frank Adelstein, K.S.Gupta : “Fundamentals of Mobile and Pervasive Computing”, 1st
Edition, Tata McGraw Hill 2005.
REFERENCES:
1. Randall k. Nichols, Panos C. Lekkas : “Wireless Security Models, Threats and Solutions”, 1st
Edition, Tata McGraw Hill, 2006.
2. Bruce Potter and Bob Fleck : “802.11 Security” , 1st Edition, SPD O‟REILLY 2005.
3. James Kempf: “Guide to Wireless Network Security, Springer. Wireless Internet Security –
Architecture and Protocols”, 1st Edition, Cambridge University Press, 2008.
176
MCO 061 A ETHICAL HACKING AND DIGITAL
FORENSIC TOOLS LAB
0-0-2
Course Objectives:
The main objective this practical session is that students will get the exposure to various forensic
tools and scripting languages.
The following programs should be implemented preferably on platform Windows/Unix through
perl, shell scripting language and other standard utilities available with UNIX systems. :-
Part A :
1. Write a perl script to concatenate ten messages and transmit to remote server
a) Using arrays b) Without using arrays.
2. Write a perl script to implement following functions:
a) Stack functions b) File functions c) File text functions
d) Directory functions e) Shift, unshift, Splice functions.
3. Write a Perl script to secure windows operating systems and web browser by disabling Hardware
and software units.
4. Write a perl script to implement Mail bombing and trace the hacker.
5. Write a shell script to crack UNIX login passwords and trace it when breaking is happened.
Part B: Exposure on Forensic tools.
1. Backup the images file from RAM using Helix3pro tool and show the analysis.
2. Introduction to Santhoku Linux operating system and features extraction.
3. Using Santoku operating system generates the analysis document for any attacked file from by
taking backup image from RAM.
4. Using Santoku operating system generates the attacker injected viewing java files.
5. Using Santoku operating system shows how attackers opened various Firefox URL‟s and pdf
document JavaScript files and show the analysis.
6. Using Santoku operating System files show how an attacker connected to the various network
inodes by the specific process.
7. Using exiftool (-k) generate the any picture hardware and software.
8. Using deft_6.1 tool recover the attacker browsing data from any computer.
Using Courier tool Extract a hacker secret bitmap image hidden data.
10. Using sg (Stegnography) cyber Forensic tool hide a message in a document or any file.
11. Using sg cyber Forensic tool unhide a message in a document or any file.
12. Using Helix3pro tool show how to extract deleted data file from hard disk or usb device.
13. Using Ghostnet tool hide a message into a picture or any image file.
14. Using kgbkey logger tool record or generate an document what a user working on system
15. Using pinpoint metaviewr tool extract a metadata from system or from image file.
16. Using Bulk Extractor tool extract information from windows file system.
177
Course Outcomes:
By the completion of this laboratory session Student
1. Will get the practical exposure to forensic tools.
2. Will gain the knowledge on perl and Unix scripting languages to implement various security
attacks.
3. Will get the ideas in various ways to trace an attacker.
178
MCO 061 A Cryptography And Security Lab 0-0-2
Course Educational Objectives:
The objective of this course is that to understand the principles of encryption algorithms,
conventional and public key cryptography practically with real time applications.
The following programs should be implemented preferably on platform Windows/Unix using C
language (for 1-5) and other standard utilities available with UNIX systems (for 6-15) :-
1. Implement the encryption and decryption of 8-bit data using Simplified DES Algorithm (created
by Prof. Edward Schaefer) in C
2. Write a program to break the above DES coding
3. Implement Linear Congruential Algorithm to generate 5 pseudo-random numbers in C
4. Implement Rabin-Miller Primality Testing Algorithm in C
5. Implement the Euclid Algorithm to generate the GCD of an array of 10 integers in C
6. a)Implement RSA algorithm for encryption and decryption in C
b) In an RSA System, the public key of a given user is e=31,n=3599.
Write a program to find private key of the User.
7. Configure a mail agent to support Digital Certificates, send a mail and verify the correctness of
this system using the configured parameters.
8. Configure SSH (Secure Shell) and send/receive a file on this connection to verify the
correctness of this system using the configured parameters.
9. Configure a firewall to block the following for 5 minutes and verify the correctness of this
system using the configured parameters: (a) Two neighborhood IP addresses on your LAN (b) All
ICMP requests (c) All TCP SYN Packets
10. Configure S/MIME and show email-authentication.
11. Implement encryption and decryption with openssl.
12. Implement Using IP TABLES on Linux and setting the filtering rules.
13. Implementation of proxy based security protocols in C or C++ with features like
Confidentiality, integrity and authentication.
14. Working with Sniffers for monitoring network communication (Ethereal)
15. Using IP TABLES on Linux and setting the filtering rules
Course Outcomes:
By the end of the course students will
1. Know the methods of conventional encryption.
2. Understand the concepts of public key encryption and number theory
3. Understand various applications of cryptography and security issues practically.
179
M.Tech. in Computer Science & Engineering (Cyber Security) Semester III
MCO 063A Secure Protocol Design 4-0-0
Course Objective :
The main objective of this course is that to explore various protocols and design of various
protocols with deeper security.
Module 1:
OSI:ISO Layer Protocols:-Application Layer Protocols-TCP/IP, HTTP, SHTTP,
LDAP,MIME,-POP&POP3-RMON-SNTP-SNMP. Presentation Layer
Protocols-Light Weight Presentation Protocol Session layer protocols.
Module 2:
RPC protocols-transport layer protocols-ITOT, RDP, RUDP, TALI, TCP/UDP,
compressed TCP. Network layer Protocols – routing protocols-border gateway
protocol-exterior gateway protocol-internet protocol IPv4- IPv6- Internet
Message Control Protocol- IRDP- Transport Layer Security-TSL-SSL-DTLS
Module 3:
Data Link layer Protocol – ARP – In ARP – IPCP – IPv6CP – RARP – SLIP
.Wide Area and Network Protocols- ATM protocols – Broadband Protocols –
Point to Point Protocols – Other
WAN Protocols- security issues.
Module 4:
Local Area Network and LAN Protocols – ETHERNET Protocols – VLAN
protocols – Wireless LAN Protocols – Metropolitan Area Network Protocol –
Storage Area Network and SAN
Module 5:
Protocols -FDMA, WIFI and WIMAX Protocols- security issues. Mobile IP –
Mobile Support Protocol for IPv4 and IPv6 – Resource Reservation Protocol.
Multi-casting Protocol – VGMP – IGMP – MSDP .Network Security and
Technologies and Protocols – AAA Protocols – Tunneling Protocols – Secured
Routing Protocols – GRE- Generic Routing Encapsulation – IPSEC – Security.
At the end of the course, the student should be able to:
1. Get the exposure to various protocols.
2. Gain knowledge on various secure mechanisms through set of protocols.
3. Efficiently design new set of protocols.
4. Learn Security issues and overcome means with protocols.
TEXT BOOKS:
1. Jawin: “Networks Protocols Handbook”, 3rd Edition, Jawin Technologies Inc., 2005.
2. Bruce Potter and Bob Fleck : “802.11 Security”, 1st Edition, O‟Reilly Publications, 2002.
REFERENCES:
1. Ralph Oppliger :“SSL and TSL: Theory and Practice”, 1st Edition, Arttech House, 2009.
2. Lawrence Harte: “Introduction to CDMA- Network services Technologies and Operations”,
1st Edition, Althos Publishing, 2004.
3. Lawrence Harte: “Introduction to WIMAX”, 1st Edition, Althos Publishing, 2005.
180
M.Tech. in Computer Science & Engineering (Cyber Security) Semester III
MCO 064A Digital Forensics 4-0-0
Course Objective:
1. The main objective of the course is to introduce the students to bring awareness in crimes and
tracing the attackers.
2. Define digital forensics from electronic media.
3. Describe how to prepare for digital evidence investigations and explain the differences
between law enforcement agency and corporate investigations.
4. Explain the importance of maintaining professional conduct
Module 1:
Introduction & evidential potential of digital devices – Key developments,
Digital devices in society, Technology and culture, Comment, Closed vs. open
systems, evaluating digital evidence potential.
Device Handling & Examination Principles: Seizure issues, Device
identification, Networked devices, Contamination, Previewing, Imaging,
Continuity and hashing, Evidence locations.
Module 2:
A sevenelement security model, A developmental model of digital systems,
Knowing, Unknowing, Audit and logs , Data content, Data context. Internet &
Mobile Devices The ISO / OSI model, The internet protocol suite, DNS, Internet
applications, Mobile phone PDAs, GPS, Other personal technology.
Module 3:
Introduction to Computer Forensics, Use of Computer Forensics in Law
Enforcement, Computer Forensics Assistance to Human Resources /
Employment Proceedings, Computer Forensics Services, Benefits of
Professional Forensics Methodology, Steps Taken by Computer Forensics
Specialists, Who Can Use Computer Forensic Evidence?, Case Histories, Case
Studies.
Module 4:
Types of Military Computer Forensic Technology, Types of Law Enforcement:
Computer Forensic Technology, Types of Business Computer Forensic
Technology, Specialized Forensics Techniques, Hidden Data and How to Find
It, Spyware and Adware, Encryption Methods and Vulnerabilities, Protecting
Data from Being Compromised, Internet Tracing Methods 65.
Module 5:
Homeland Security Systems. Occurrence of Cyber Crime, Cyber Detectives,
Fighting Cyber Crime withRisk Management Techniques, Computer Forensics
Investigative Services, Forensic Process Improvement, Course Content, Case
Histories.
At the end of the course, the student should be able to:
1. Utilize a systematic approach to computer investigations.
2. Utilize various forensic tools to collect digital evidence.
3. Perform digital forensics analysis upon Windows, MAC and LINUX operating systems
4. Perform email investigations.
5. Analyze and carve image files both logical and physical
181
TEXT BOOKS:
1. Angus M.Mashall, “Digital Forensics”, 2nd Edition,Wiley-Blackwell, A John Wiley & Sons
Ltd Publication, 2008.
2. John R. Vacca, “ Computer forensics : Computer Crime Scene Investigation”, 2nd Edition,
Charles River Media, Inc. Boston, Massachusetts.
REFERENCES:
1. Michael G. Noblett; Mark M. Pollitt, Lawrence A. Presley (October 2000), "Recovering and
examining computer forensic evidence", Retrieved 26 July 2010.
2. Leigland, R (September 2004). "A Formalization of Digital Forensics".(Pdf document ).
3. Geiger, M (March 2005). "Evaluating Commercial Counter-Forensic Tools" (Pdf document).
182
M.Tech. in Computer Science & Engineering (Cyber Security) Semester III
MCO 065A Intrusion Detection Systems 4-0-0
Course Objective:
1. Understand when, where, how, and why to apply Intrusion Detection tools and techniques in
order to improve the security posture of an enterprise.
2. Apply knowledge of the fundamentals and history of Intrusion Detection in order to avoid
common pitfalls in the creation and evaluation of new Intrusion Detection Systems
3. Analyze intrusion detection alerts and logs to distinguish attack types from false alarms
Module 1:
History of Intrusion detection, Audit, Concept and definition , Internal and
external threats to data, attacks, Need and types of IDS, Information sources Host
based information sources, Network based information sources.
Module 2:
Intrusion Prevention Systems, Network IDs protocol based IDs ,Hybrid IDs,
Analysis schemes,
thinking about intrusion. A model for intrusion analysis , techniques Responses
requirement of responses, types of responses mapping responses to policy
Vulnerability analysis, credential analysis non credential analysis
Module 3:
Introduction to Snort, Snort Installation Scenarios, Installing Snort, Running
Snort on Multiple Network Interfaces, Snort Command Line Options. Step-By-
Step Procedure to Compile and Install Snort Location of Snort Files, Snort
Modes Snort Alert Modes
Module 4:
Working with Snort Rules, Rule Headers, Rule Options, The Snort Configuration
File etc. Plugins, Preprocessors and Output Modules, Using Snort with MySQL
Module 5:
Using ACID and Snort Snarf with Snort, Agent development for intrusion
detection, Architecture models of IDs and IPs.
At the end of the course, the student should be able to:
1. Explain the fundamental concepts of Network Protocol Analysis and demonstrate the skill to
capture and analyze network packets.
2. Use various protocol analyzers and Network Intrusion Detection Systems as security tools to
detect network attacks and troubleshoot network problems.
TEXT BOOKS:
1. Rafeeq Rehman : “ Intrusion Detection with SNORT, Apache, MySQL, PHP and ACID,” 1st
Edition, Prentice Hall , 2003.
REFERENCES:
1. Christopher Kruegel,Fredrik Valeur, Giovanni Vigna: “Intrusion Detection and Correlation
Challenges and Solutions”, 1st Edition, Springer, 2005.
2. Carl Endorf, Eugene Schultz and Jim Mellander “ Intrusion Detection & Prevention”, 1st
Edition, Tata McGraw-Hill, 2004.
183
3. Stephen Northcutt, Judy Novak : “Network Intrusion Detection”, 3rd Edition, New Riders
Publishing, 2002.
4. T. Fahringer, R. Prodan, “A Text book on Grid Application Development and Computing
Environment”. 6th Edition, KhannaPublihsers, 2012.
184
M.Tech. in Computer Science & Engineering (Cyber Security) Semester III
MCO 66A Database and Application Security 4-0-0
Course Objective:
To understand the security issues and solutions for Database, Multilevel Database,
Distributed database, Outsourced Database and Data Warehouse.
Module 1:
Introduction to Database – Relational Database & Management System – ACID
Properties,Normalization, RAID, Relational Algebra, Query tree, Data
Abstraction ( Physical Level, Logical Level & View Level) - Multi-level
Database, Distributed Database
Module 2:
Security issues in Database – Polyinstantiation - Integrity Lock - ensitivity Lock
– Security Models – Access Control (Grant & Revoke Privileges) - Statistical
Database, Differential Privacy. Distributed Database Security.
Module 3:
Outsourced Database and security requirements – Query Authentication
Dimension –Condensed RSA, Merkle Tree, B+ Tree with Integrity and
Embedded Merkle B-Tree – Partitioning & Mapping - Keyword Search on
Encrypted Data (Text file).
Module 4:
Security in Data Warehouse & OLAP – Introduction, Fact table, Dimensions,
Star Schema,Snowflake Schema, Multi-Dimension range query, Data cube -
Data leakage in Data Cube, 1-d inference and m-d inference – Inference Control
Methods.
Module 5:
XML – Introduction about XML – Access Control Requirements, Access
Control Models:Fine Grained XML Access Control System.
At the end of the course, the student should be able to:
Reference Books
1. Michael Gertz and Sushil Jajodia (Editors), Handbook of Database Security: Applications
and Trends , ISBN-10: 0387485325. Springer, 2007
2. Osama S. Faragallah, El-Sayed M. El-Rabaie, Fathi E. Abd El-Samie, Ahmed I. Sallam,
and Hala S. El-Sayed, Multilevel Security for Relational Databases by; ISBN 978-1-4822-
0539-8. CRC Press, 2014.
3. Bhavani Thuraisingham, Database and Applications Security: Integrating Information
Security and Data Management, CRC Press, Taylor & Francis Group, 2005.
185
M.Tech. in Computer Science & Engineering (Cyber Security) Semester III
MCO 067A Data Science for Security Analysis 4-0-0
Course Objective:
Module 1:
Handling Streaming Data - In-memory Analytics using Spark, Introduction to
Redis, Using Redis in Spark for In-mem analytics, Message Brokers (MQTT,
Kafka, Active MQ), CMS,HLL algorithms, Social media Analytics, Streaming
Sensor Data Analytics, Introduction to Streaming Algorithms
Module 2:
Advanced Hadoop& MR, Implementing complex algorithms using MR,
Analytics HDFS data in Spark (in-memory) using Shark and Spark SQL,
Implementing Slowly changing dimensions in Hadoop based Data warehouses.
Module 3:
Big Data Warehouse - Hadoop Ecosystem, HBase, Pig & Pig Latin, Sqoop,
ZooKeeper, Hue,Hive,Flume,Oozie
Module 4:
Security Concerns in Big data, Visualization techniques in Big Data Analytics
Module 5:
Case Study & Implementation
At the end of the course, the student should be able to:
TEXTBOOKS/REFERENCES:
1. A. Rajaraman and J. D. Ullman, Mining of Massive Datasets, Cambridge University Press,
2012.
2. N. Burlingame, The Little Book of Big Data, New Street Communications, LLC,
Wickford, 2012.
186
M.Tech. in Computer Science & Engineering (Cyber Security) Semester III
MCO 068A Vulnerability Discovery and Exploit Development 4-0-0
Course Objective:
Objective of this course is to focus on a comprehensive coverage of software exploitation. In
addition, this course will present different domains of code exploitation and how they can be
used together to test the security of an application.
Module 1:
Background- Vulnerability Discovery Methodologies, What is Fuzzing,
Fuzzing Methods and Fuzzer Types, Data Representation and Analysis,
Requirements for Effective Fuzzing
Targets and Automation- Automation and Data Generation, Environment
Variable and Argument Fuzzing, Environment Variable and Argument Fuzzing:
Automation, Web Application and Server Fuzzing, Web Application and Server
Fuzzing: Automation, File Format Fuzzing, File Format Fuzzing: Automation
on UNIX, File Format Fuzzing: Automation on Windows, Network Protocol
Fuzzing, Network Protocol Fuzzing: Automation on UNIX, Network Protocol
Fuzzing: Automation on Windows, Web Browser Fuzzing, Web Browser
Fuzzing: Automation, In-Memory Fuzzing, In-Memory Fuzzing: Automation
Module 2:
Advanced Linux Exploitation-Linux heap management, constructs, and
environment, Navigating the heap, Abusing macros such as unlink() and
frontlink(), Function pointer overwrites, Format string exploitation, Abusing
custom doubly-linked lists, Defeating Linux exploit mitigation controls, Using
IDA for Linux application exploitation, Patch Diffing, one day Exploits and
Return Oriented Shellcode, The Microsoft patch management process and Patch
Tuesday, Obtaining patches and patch extraction, Binary diffing with BinDiff,
patchdiff2, turbodiff, and darungrim, Visualizing code changes and identifying
fixes, Reversing 32-bit and 64-bit applications and modules, Triggering patched
vulnerabilities, Writing one-day exploits, Handling modern exploit mitigation
controls.
Module 3:
Windows Kernel Debugging and Exploitation- Understanding the Windows
Kernel, Navigating the Windows Kernel, Modern Kernel protections,
Debugging the Windows Kernel, WinDbg, Analysing Kernel vulnerabilities and
Kernel vulnerability types, Kernel exploitation techniques.
Module 4:
Android Exploitation- Android Basics, Android Security Model, Introduction
to ARM, Android Development Tools, Engage with Application Security,
Android Security Assessment Tools, Exploiting Applications, Protecting
Applications, Secure Networking, Native Exploitation and Analysis.
Module 5:
iOS exploitation-Introduction to iOS hacking, iOS User Space Exploitation,
iOS Kernel Debugging and Exploitation
At the end of the course, the student should be able to:
187
1. Understand how to exploit a program and different types of software exploitation
techniques
2. Understand the exploit development process
3. Search for vulnerabilities in closed-source applications
4. Write their own exploits for vulnerable applications
Text books and References:
1. Hack I.T. - Security Through Penetration Testing, T. J. Klevinsky, Scott Laliberte and Ajay
Gupta, Addison-Wesley, ISBN: 0-201-71956-8
2. Metasploit: The Penetration Tester's Guide, David Kennedy, Jim O'Gorman, Devon Kearns,
Mati Aharoni
3. Professional Penetration Testing: Creating and Operating a Formal Hacking Lab, Thomas
Wilhelm
188
M.Tech. in Computer Science & Engineering (Cyber Security) Semester III
MCO 026A Biometric Security 4-0-0
Course Objective:
• To explain different biometrics parameters
• To design a basic biometric facility
• To participate in Bidding process and Equipment installation of Biometric Equipment
• To Administrate a Biometric Facility
UNIT 1: Explain the errors generated in biometric measurements cs: Need
UNIT 2:
Conventional techniques of authentication, challenges – legal and privacy issues
UNIT 3:
Biometrics in use: DNA, fingerprint, Iris, Retinal scan, Face, hand geometry
UNIT 4:
Human gait, speech, ear. Handwriting, Keystroke dynamics
UNIT 5:
Signature Multimodal biometrics: Combining biometrics, scaling issues.
Biometric template security
Outcomes
At the end of this course Students will be able to:
• Explain different biometrics parameters
• Design a basic biometric facility
• Participate in Bidding process and Equipment installation of Biometric Equipment
• Administrate a Biometric Facility
• Understand the privacy challenges of Biometrics
Texts/References:
2. Julian D. M. Ashbourn, Biometrics: Advanced Identify Verification: The Complete
Guide
Reference Books:
1. DavideMaltoni (Editor), et al, Handbook of Fingerprint Recognition
189
2. L.C. Jain (Editor) et al, Intelligent Biometric Techniques in Fingerprint and Face Recognition
3. John Chirillo, Scott Blaul, Implementing Biometric Security
190
M.Tech. in Computer Science & Engineering (Cyber Security) Semester III
MCO 069A Information Security Risk Management 4-0-0
Course Objective
To understand and development of concepts required for risk-based planning and risk
management of computer and information systems.
Module 1: An Introduction to Risk Management: Introduction to the Theories of Risk
Management; The Changing Environment; The Art of Managing Risks.
Module 2:
The Threat Assessment Process: Threat Assessment and its Input to Risk
Assessment; Threat Assessment Method; Example Threat Assessment
Module 3:
Vulnerability Issues: Operating System Vulnerabilities; Application
Vulnerabilities; Public Domain or Commercial Off-the-Shelf Software;
Connectivity and Dependence; Vulnerability assessment for natural disaster,
technological hazards, and terrorist threats; implications for emergency
response, vulnerability of critical infrastructures
Module 4:
The Risk Process: What is Risk Assessment? Risk Analysis; Who is
Responsible?
Module 5:
Tools and Types of Risk Assessment: Qualitative and Quantitative risk
Assessment; Policies, Procedures, Plans, and Processes of Risk Management;
Tools and Techniques; Integrated Risk Management; Future Directions: The
Future of the Risk Management.
At the end of the course, the student should be able to:
1. The ability to identify, analyze and articulate the importance of managing IS-related risk and
security issues in organizations, and the relationship between these and the achievement of
business value from IS/IT investments
2. The ability to identify, analyze, synthesize and evaluate the costs of not appropriately
identifying and managing risk and security concerns in projects and organizations, resulting in
IS/IT failures, dysfunctional systems, and systems which fail to deliver value to key
stakeholders
3. The practical ability to develop and document IS/IT risk and security management plans that
detail contingency planning strategies and practices
4. The ability to identify, analyze, synthesize and articulate the major theories and concepts
associated with IS failure and the management of IS risk, including factors argued to lead to
unsatisfactory outcomes with respect to IS/IT and Information Security
Text books:
1. Malcolm Harkins, Managing Risk and Information Security, Apress, 2012.
2. Daniel Minoli, Information Technology Risk Management in Enterprise Environments, Wiley,
2009.
191
Reference books:
1. Andy Jones, Debi Ashenden ,Risk Management for Computer Security: Protecting Your Network
& Information Assets, , 1st Edition, Butterworth-heinemann, Elsevier, 2005.
2. Andreas Von Grebmer, Information and IT Risk Management in a Nutshell: A pragmatic approach
to Information Security, 2008, Books On Demand Gmbh.
192
M.Tech. in Computer Science & Engineering (Cyber Security) Semester III
MCO 028A PKI and Trust Management 4-0-0
Course Objective:
At the end of this course Students will be able to:
• Understand trust in a Digital World.
• Understand the foundations of Cryptography and elements of PKI.
• Learn about the various trust models.
• Understand the legal aspects.
UNIT 1:
Public Key Cryptography: Symmetric v/s Asymmetric ciphers, Secret key, New
Directions: Public key, public/private key pair, Services of public key
cryptography
UNIT 2:
Algorithms: Diffie Hellman key exchange algorithm, RSA algorithm. Digital
certificate and Public Key Infrastructure: Digital Certificates, private key
management,
UNIT 3:
the PKIX model, public key cryptography standards, Certification authority,
certificate repository, certificate revocation, cross certification. Hierarchical PKI,
Mesh PKI, What does PKI offer, Simple Public Key Infrastructure
UNIT 4:
Pretty Good Privacy, X509 Version 3 Public Key Certificate, Secure Electronic
Transaction Certificate, Attribute Certificate, Certificate Policies
UNIT 5:
Trust Model: Strict hierarchy of certification authority, Loose hierarchy of
certification authority, Four-Corner Model
Outcomes:
At the end of this course Students will be able to:
• Understand trust in a Digital World.
• Understand the foundations of Cryptography and elements of PKI.
• Learn about the various trust models.
• Understand the legal aspects.
Text Books:
1. AtulKahate, “Cryptography and Network Security”, TMH
2.Understanding PKI: Concepts, Standards and Deployment, Considerations, Second Edition by
Crlisle Adams, Steve Loyd, Addision-Wesely Professional