M. Tech. in Computer Science and Engineering Academic ...

9

School of Engineering & Technology

Syllabi and Course Structure

M. Tech. in Computer Science and

Engineering

Academic Programs

April, 2019

10


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

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M.Tech. inComputer Science & Engineering

Second Semester

SECOND SEMESTER


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


M.Tech. inComputer Science & Engineering

Third Semester

THIRD SEMESTER


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

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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:


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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.

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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:


1. Understand the state of the art in operating systems and distributed systems, and how to


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

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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:


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:

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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


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.

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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.

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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:


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• 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


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:


• 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.

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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


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


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.


• Learn the fundamentals of data models and to conceptualize and depict a database system

using ER diagram.

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• 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.


• 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.

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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:


• Gain insights into how scientific research is conducted.

• Help in critical review of literature and assessing the research trends, quality and


• 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


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.


• 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











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


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


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


• 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


• 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


• 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


• 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:


• 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



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


M.Tech. inComputer Science & Engineering (CLOUD COMPUTING)

Course Structure

First Semester

First Semester


MCO 056A Advanced Data Structure and Algorithm Design 4 0 0 4

MCO 007A Advance Data Communication network 4 0 0 4



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




TOTAL 16 0 06 22

55



Second Semester

SECOND SEMESTER


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


Applications Lab

0 0 1 1

MCO 107A Cloud Storage Infrastructures lab 0 0 2 2



TOTAL 15 0 07 22

56



Third Semester

THIRD SEMESTER



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


TOTAL 16 0 0 28

Fourth Semester

FOURTH SEMESTER


TOTAL 0 0 0 28

57

M.Tech. in Computer Science & Engineering (Data Analytics) Semester I


Course Objective





Module 1:







Module 2:






Module 3:







Module 4:




Module 5:






Outcomes:

58






Text Books:





Reference Books:



Education.





Hill.



59



Course Objective:






Module 1:







Module 2:







Module 3:










Module 4:











60

Module 5:








Outcomes:





in particular.


Suggested Books






-Hill.


61



Course Objective:





Module 1:



Module 2:





Requirements

Module 3:





Module 4:






Computing.

Module 5:





Outcomes:






Text books:

62










63


MCO 014A Advance Topics in Data Mining and Warehousing 3-0-0

Course Objective:


and application.




UNIT 1:






UNIT 2:





UNIT 3:



data visualization.




UNIT 4:










UNIT 5:



indexing schemes.




visualization.

Outcomes:

64



application.





Text Books



Reference Books:




65



Course Objective



processing system



UNIT 1:





Mathematical tools.

UNIT 2:






restoration models.

UNIT 3:





UNIT 4:



Wavelet Transforms,




UNIT 5:



Analysis, Image Fusion, Steganography, Colour Image Processing. Outcomes:




system



66

Text Books:




Course Objective:




UNIT 1:







DES

UNIT 2:







UNIT 3:






UNIT 4:



Infrastructure.



UNIT 5:






67

Outcomes:





Suggested Books:


Education.







List of Experiments














68











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

http://strategicitplanningguide.com/

http://www.cio.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


M. Tech. in Artificial Intelligence

84

(Computer Science)

Academic Programmes

April, 2021

85


M.Tech. inComputer Science & Engineering (Artificial Intelligence) Course Structure

First Semester

First Semester


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


M.Tech. in Computer Science & Engineering (Artificial Intelligence)

Second Semester

SECOND SEMESTER


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


M.Tech. in Computer Science & Engineering (Artificial Intelligence)

Third Semester

THIRD SEMESTER


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


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

89

CO2 H H M H M H

CO3 H H M H M H

H = Highly Related; M = Medium L = Low

90

M.Tech. in Computer Science & Engineering (Artificial Intelligence) Semester I


Course Objective





Module 1:







Module 2:






Module 3:







Module 4:




Module 5:






91

Text Books:





7.

Reference Books:



Education.





Hill.

16. Mahbub Hassan, Raj Jain, “High Performance TCP/IP Networking, Concepts, Issues, and


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


Course

Outcome


Specific

Outcome


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



Course Objective:

• To introduce the state of the art in operating systems and distributed systems,

• Learn how to design modern operating systems.



• To investigate novel ideas in operating systems through a semester-long research project.

Module 1:







Module 2:







93

Module 3:










Module 4:











Module 5:








Suggested Books






-Hill.


Course Outcomes:


94

CO1. Understand the state of the art in operating systems and distributed systems, and how to


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.


Course

Outcome


Specific

Outcome


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


95


MCO 014A Advanced Data Mining and Warehousing 3-0-0

Course Objective:

• To compare and contrast different conceptions of data mining



• To describe how to extend a relational system

• To find patterns using association rules.

UNIT 1:






UNIT 2:



data warehouse architecture, metadata, metadata extraction, implementing the


UNIT 3:



data visualization.




UNIT 4:










UNIT 5:



indexing schemes.




visualization.

96

Text Books



Reference Books:




Course Outcomes:


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.


Course

Outcome


Specific

Outcome


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


97

M.Tech. in Computer Science & Engineering (Artificial Intelligence)- Semester I


Communication

3-0-0

Course Objective:






Module 1:




Module 2:




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:


Schedule, Characteristics of a good test, Statistics: Descriptive and Inferential

Statistics,Data Analysis, Report Writing, Results and References

Module 5:




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


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


CO1

CO2

98

CO3

CO4

CO5




List of Experiments














99


MCO Advanced Technology Lab 0-0-2


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


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


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


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


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



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



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


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


109


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


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



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



M. Tech. in Data Analytics

(Computer Science)

Academic Programs

April, 2019

115


M.Tech. in Computer Science & Engineering (Data Analytics)

Course Structure

First Semester

First Semester






Elective I

4 0 0 4


and warehousing






TOTAL 16 0 6 22

116



Second Semester

SECOND SEMESTER


MCO 071A Foundations of Data Science 4 0 0 4

MCO 072A Programming for Data Analytics 4 0 0 4


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


Applications Lab

0 0 1 1

MCO 077A Programming for Data Analytics Lab 0 0 2 2


TOTAL 15 0 7 22

117



Third Semester

THIRD SEMESTER


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


TOTAL 8 0 0 28

Fourth Semester

FOURTH SEMESTER


TOTAL 0 0 0 28

118



Course Objective





Module 1:







Module 2:






Module 3:







Module 4:




Module 5:






119

Outcomes:






Text Books:





Reference Books:



Education.





Hill.



120



Course Objective:






Module 1:







Module 2:







Module 3:










Module 4:











121

Module 5:








Outcomes:





in particular.


Suggested Books






-Hill.


122



Course Objective:





Module 1:



Module 2:





Requirements

Module 3:





Module 4:






Computing.

Module 5:





Outcomes:






Text books:

123










124



Course Objective:


and application.




UNIT 1:






UNIT 2:





UNIT 3:



data visualization.




UNIT 4:










UNIT 5:



indexing schemes.




visualization.

Outcomes:

125



application.





Text Books



Reference Books:




126



Course Objective



processing system



UNIT 1:





Mathematical tools.

UNIT 2:






restoration models.

UNIT 3:





UNIT 4:



Wavelet Transforms,




UNIT 5:




127




system



Text Books:




Course Objective:




UNIT 1:







DES

UNIT 2:







UNIT 3:






128

UNIT 4:



Infrastructure.



UNIT 5:






Outcomes:





Suggested Books:


Education.







List of Experiments









129






130











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.


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


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.


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



Communication

3-0-0

Course Objective:






Module 1:




Module 2:





Module 3:



Module 4:


Schedule,Characteristics of a good test, Statistics: Descriptive and Inferential

Statistics

Data Analysis, Report Writing, Results and References,

Module 5:




Outcome:






Identify the influencing factor or determinants of research parameters.

134


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


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


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.


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


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


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


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


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


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


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.


• 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


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.


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


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


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


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


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


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


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


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.


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


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.


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



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


M.Tech. in Computer Science & Engineering (Cyber Security)

Course Structure

First Semester

First Semester






Elective I

4 0 0


and warehousing






TOTAL 16 0 06 22

150


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


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


TOTAL 15 0 07 22

151


M.Tech. inComputer Science & Engineering (Cyber Security)

Third Semester

THIRD SEMESTER


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


TOTAL 16 0 0 28

Fourth Semester

FOURTH SEMESTER


TOTAL 0 0 0 28

152

M.Tech. in Computer Science & Engineering (Cyber Security) Semester I


Course Objective





Module 1:







Module 2:






Module 3:







Module 4:




Module 5:






Outcomes:


153





Text Books:





Reference Books:



Education.





Hill.



154



Course Objective:






Module 1:







Module 2:







Module 3:










Module 4:











155

Module 5:








Outcomes:





in particular.


Suggested Books






-Hill.


156



Course Objective:





Module 1:



Module 2:





Requirements

Module 3:





Module 4:






Computing.

Module 5:





Outcomes:






Text books:

157










158



Course Objective:


and application.




UNIT 1:






UNIT 2:





UNIT 3:



data visualization.




UNIT 4:










UNIT 5:



indexing schemes.




visualization.

Outcomes:

159



application.





Text Books



Reference Books:




160



Course Objective



processing system



UNIT 1:





Mathematical tools.

UNIT 2:






restoration models.

UNIT 3:





UNIT 4:



Wavelet Transforms,




UNIT 5:







system



Text Books:

161




Course Objective:




UNIT 1:







DES

UNIT 2:







UNIT 3:






UNIT 4:



Infrastructure.



UNIT 5:






Outcomes:




162


Suggested Books:


Education.





163



List of Experiments














164











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.


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


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


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


Communication

3-0-0

Course Objective:






Module 1:




Module 2:





Module 3:



Module 4:


Schedule,Characteristics of a good test, Statistics: Descriptive and Inferential

Statistics

Data Analysis, Report Writing, Results and References,

Module 5:




Outcome:






Identify the influencing factor or determinants of research parameters.

170


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:


• 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


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.


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


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.


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.


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


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.


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


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.


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


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.


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


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


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


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


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


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


• 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


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.


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


MCO 028A PKI and Trust Management 4-0-0

Course Objective:


• 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:


• 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:


2.Understanding PKI: Concepts, Standards and Deployment, Considerations, Second Edition by

Crlisle Adams, Steve Loyd, Addision-Wesely Professional

M. Tech. in Computer Science and Engineering Academic ...

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