m.tech. (computer science and engineering)1. nils j. nilsson: “principles of artificial...

Karnatak Law Society’s

GOGTE INSTITUTE OF TECHNOLOGY

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

M.Tech. (Computer Science and Engineering)

Second Semester

S.No. Course

Code Course

Credits Total

credits

Contact

Hrs/wk

Marks

L - T - P CIE SEE TOTAL

1. SCS21 Data Science and Analytics PC1 3 - 1 - 0 4 5 50 50 100

2. SCS22 Advances in Computer Networks PC2 3 - 1 - 0 4 5 50 50 100

3. SCS23 Artificial Intelligence & Agent

Technology PC3 3 - 1 - 0 4 5

50 50 100

4. SCS24 Machine Learning Techniques PC4 4 - 0 - 0 4 4 50 50 100

5. SCS25X Elective – II PE- B 4 - 0 - 0 4 4 50 50 100

6. SCS26 Machine Learning Laboratory 0 - 0 - 2 2 4 25 25 50

7. SCS27 Seminar-II 0 - 1 - 0 1 - 25 - 25

8. PTA28 Mini Project-II Mandatory 0 - 0 - 2 2 4 25 - 25

Total 25 31 325 275 600

ELECTIVE – II

SCS251 Wireless Networks & Mobile Computing

SCS252 Network Programming and Internet

Technologies

SCS253 Information Storage Management

SCS254 Web Security

Second Semester

Data Science And Analytics

Subject Code: SCS21 Credits: 4

Course Type: PC1 CIE Marks: 50

Hours/week: L – T – P 3 – 1 – 0 SEE Marks: 50

Total Hours: 40 SEE Duration: 3

Course Learning Objectives:

1. To introduce the fundamentals of Data Analytics life cycle.

2. To present Analytics methods and tools of analysis.

Detailed Syllabus:

UNIT I 08 hours

Introduction to Big Data Analytics: Big Data Overview, State of the Practice in Analytics,

Key Roles for the New Big Data Ecosystem, Examples of Big Data Analytics.

Data Analytics Lifecycle: Data Analytics Lifecycle Overview, Phase 1: Discovery, Phase 2:

Data Preparation, Phase 3: Model Planning, Phase 4: Model Building, Phase 5: Communicate

Results, Phase 6: Operationalize, Case Study: Global Innovation Network and Analysis

(GINA)

UNIT II 08 hours

Advanced Analytical Theory and Methods: Association Rules: Overview, Apriori

Algorithm, Evaluation of Candidate Rules, Applications of Association Rules, An Example:

Transactions in a Grocery Store, Validation and Testing, Diagnostics.

Self-Study: Case study: Global innovation network and analysis (GINA) 02 Hours

UNIT III 08 hours

Advanced Analytical Theory and Methods: Classification: Diagnostics of Classifiers,

Additional Classification Methods, Time Series Analysis, Overview of Time Series Analysis,

ARIMA Model, Additional Methods.

Self-Study: Review of Basic Analytic methods using R: Introduction to R, Exploratory

data analysis, statistical methods for evaluation. 02 Hours

UNIT IV 10 hours

Advanced Analytical Theory and Methods: Text Analysis: Text Analysis Steps, A Text

Analysis Example, Collecting Raw Text, Representing Text, Term Frequency - Inverse

Document Frequency (TFIDF), Categorizing Documents by Topics, Determining Sentiments,

Gaining Insights.

UNIT V 10 hours

Advanced Analytics - Technology and Tools: In-Database Analytics: S Analytics for

Unstructured Data, The Hadoop Ecosystem, NoSQL,QL Essentials, In-Database Text

Analysis

Prerequisite:

1. Big data management

2. Artificial intelligence

3. Probability and Statistics

TEXT BOOK:

1. Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and

Presenting Data by EMC Education.

REFERENCE BOOKS:

1. Data Science from Scratch: Joel Grus, O’reilly Publication.

2. Data Science for Business: Foster Provost.

3. Doing Data Science - Cathy O'neil.

Course Outcome (Cos): Students should be able to:

1. To explain the importance of Data Analytics.[L1]

2. To discover, prepare and analyze data in a given application domain.[L3]

3. To demonstrate application of text analysis and discover patterns.[L3]

Scheme of Continuous Internal Evaluation (CIE):

Components Average of best

two tests out of

three

Average of

two

assignments

Quiz/Seminar/

Project

Total

Marks

Maximum

Marks

30 10 10 50

Scheme of Semester End Examination (SEE):

Semester end examination will be conducted for 100 marks which will be converted into 50

marks. SEE question paper will have two compulsory questions (any 2 units) and choice will

be given in the remaining three units.

Second Semester

Advances in Computer Networks


Course Type: PC CIE Marks: 50


Total Hours: 40 SEE Duration: 3 Hours

Course Objectives:

1. To become familiar with the basics of Computer Networks

2. To understand various Network architectures

3. To learn the concepts of fundamental protocols

4. To understand the network traffic, congestion, controlling

and resource allocation.

Detailed Syllabus:

UNIT I 8 Hours

Foundation

Building a Network, Requirements, Perspectives, Scalable Connectivity, Cost-

Effective Resource sharing, Support for Common Services,

Manageability,Performance, Bandwidth and Latency, Delay X Bandwidth Product,

Perspectives on Connecting, Classes of Links, Reliable Transmission, Stop-and-

Wait, Sliding Window, and Concurrent Logical Channels.

Self Study: Protocol layering 2 Hours

Unit II 8 Hours

Internetworking-I

Switching and Bridging, Datagram’s, Virtual Circuit Switching, Basic

Internetworking (IP), What is an Internetwork ?, Service Model, Global Addresses,

Datagram Forwarding in IP, sub netting and classless addressing, Address

Translation(ARP), Host Configuration(DHCP), Error Reporting(ICMP), Virtual

Networks and Tunnels.

Self Study: Source Routing 2 Hours

Unit III 8 Hours

Internetworking-II

Network as a Graph, Distance Vector (RIP), Link State (OSPF), Metrics, The Global

Internet, Routing Areas, Routing among Autonomous systems (BGP), IP Version

6(IPv6)

Self Study: Mobilty and Mobile IP 2 Hours

Unit IV 8 Hours

End-to-End Protocols

Simple Demultiplexer (UDP), Reliable Byte Stream(TCP), End-to-End Issues,

Segment Format, Connecting Establishment and Termination, Triggering

Transmission, Adaptive Retransmission, TCP Extensions, Fair Queuing, TCP

Congestion Control, Additive Increase/ Multiplicative Decrease, Slow Start, Fast

Retransmit and Fast Recovery

Self Study: Queuing Disciplines, FIFO 2 Hours

Unit V 8 Hours

Congestion Control and Resource Allocation

Congestion-Avoidance Mechanisms, DEC bit, Random Early Detection (RED),

Source-Based Congestion Avoidance. The Domain Name System(DNS),Electronic

Mail(SMTP,POP,IMAP,MIME),World Wide Web(HTTP),Network

Management(SNMP)

Self Study: Traditional and Multimedia Applications 2 Hours

Course Outcomes:

The students should be able to:

1. List and classify network services, protocols and architectures, explain why they

are layered [L1].

2. Choose key Internet applications and their protocols, and apply to develop their

own applications (e.g. Client Server applications, Web Services) using the sockets

API [L4, L3].

3. Explain develop effective communication mechanisms using techniques like

connection establishment, queuing theory, recovery etc. [L1].

4. Explain various congestion control techniques [L1].

Prerequisite:

1. Knowledge of Computer Networks.

Text books:

1. Larry Peterson and Bruce S Davis “Computer Networks :A System Approach”

5th Edition, Elsevier -2014 2. Douglas E Comer, “Internetworking with TCP/IP, Principles, Protocols and

Architecture” 6th Edition, PHI – 2014

Reference Books:

1. Ulysses Black “Computer Networks, Protocols, Standards and Interfaces”, 2nd Edition-PHI

2. Behrouz A Forouzan “TCP/IP Protocol suite”, 4th Edition – Tata McGraw-Hill


The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10

marks each and quiz/course seminar/course project of 10 marks each). The weight-age of

CIE is as shown in the table below.

Component Average of

best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks

Maximum marks

Marks

30 10 10 50


Semester end examination will be conducted for 100 marks which will be converted into

50 marks. SEE question paper will have two compulsory questions (any 2 units) and

choice will be given in the remaining three units.

Second Semester

Artificial Intelligence and Agent Technology


Course Type: PC-E CIE Marks: 50



Course Objectives:

1. To implement non-trivial AI techniques in a relatively large system.

2. To understand uncertainty and problem solving techniques.

3. To understand various symbolic knowledge representation to specify domains

and reasoning tasks of a situated software agent.

4. To understand different logical systems for inference over formal domain

representations and trace how a particular inference algorithm works on a given

problem specification.

5. To understand various learning techniques and agent technology.

Detailed Syallabus:

Unit I 8 Hours

What is Artificial Intelligence: The AI Problems, The Underlying assumption, what

is an AI Technique? The Level of the model, Criteria for success, some general

references, one final word and beyond.

Problems, problem spaces, and search: Defining the problem as a state space

search, Production systems, Problem characteristics, Production system characteristics,

Issues in the design of search programs, Additional Problems.

Self Study: Intelligent Agents: Agents and Environments, The nature of

environments,The structure of agents. . 2 Hours

Text Book 1: Chapter 1 & 2 Text Book 2: Chapter 2

Unit II 8 Hours

Heuristic search techniques: Generate-and-test, Hill climbing, Best-first search,

Problem reduction, Constraint satisfaction, Mean-ends analysis.

Knowledge representation issues: Representations and mappings, Approaches to

knowledge representation, Issues in knowledge representation, The frame problem.

Logical Agents: Knowledge –based agents, the Wumpus world, Logic-

Propositional logic, Propositional theorem proving, Effective propositional model

checking, Agents based on propositional logic.

Self-study : Using predicate logic: Representing simple facts in logic, representing

instance and ISA relationships, Computable functions and predicates, Resolution,

Natural Deduction. 2 Hours

Text Book 1: Chapter 3, 4 & 5 Text Book 2: Chapter

Unit III 8 Hours

Symbolic Reasoning under Uncertainty: Introduction to non-monotonic reasoning,

Logic for non-monotonic reasoning, Implementation Issues, Augmenting a problem-

solver, Implementation:

Statistical Reasoning: Certainty factors and rule-based systems, Bayesian

Networks, Dempster-Shafer Theory.

Quantifying Uncertainty: Acting under uncertainty, Inference using full joint

distributions, Independence, Baye’s rule and its use, The Wumpus world revisited.

Self-Study: Depth-first search, Implementation: Breadth-first search. Basic probability

notation, Probability and Bayes Theorem. 2 Hours

Text Book 1: Chapter 7 & 8 Text Book 2: Chapter

Unit IV 8 Hours

Weak Slot-and-filter structures: Semantic Nets Frames.

Strong slot-and –filler structures: Conceptual dependency, scripts, CYC.

Adversarial Search: Games, Optimal Decision in Games, Alpha-Beta Pruning,

Imperfect Real-Time Decisions, Stochastic Games, Partially Observable Games, State-

Of-The-Art Game Programs, Alternative Approaches, Summary.

Text Book 1: Chapter 9 & 10 Text Book 2: Chapter 5

Unit V 7 Hours

Learning From examples: Learning decision trees, Classification with linear models,

KNN. Evaluating and choosing the best hypothesis, The theory of learning ,PAC, ,

Nonparametric models, Support vector machines, Ensemble learning.

Learning Probabilistic Models: Statistical learning, learning with complete data

Text Book 2: Chapter 18 & 20

Self-Study: Forms of learning, Supervised/Unsupervised learning, Reinforced learning,

Regression. 2 Hours

Prerequisite:

1. Discrete Mathematical Structures

2. Probability

Course Outcomes:

The students will be able to

1. Design intelligent agents for problem solving, reasoning, planning, decision

making and learning for specific design and performance constraints and

when needed, design variants of existing algorithms [L4].

2. Apply AI techniques on current applications [L3].

3. Demonstrate ability for problem solving, knowledge representation, reasoning

and learning [L3].

Text Books:

1. Elaine Rich Kevin Knight, Shivashankar B Nair: Artificial Intelligence, Tata

McGraw Hill 3rd edition 2013.

2. Stuart Russel, Peter Norvig: Artiificial Intelligence A Modern Approach, Pearson

3rd edition 2013. Reference Books:

1. Nils J. Nilsson: “Principles of Artificial Intelligence”, Elsevier, ISBN-13: 9780934613101

2. M. Tim Jones, “Artificial Intelligence: A Systems Approach”, Jones and Bartlett Publisher, 2010


The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of

10 marks each and quiz/course seminar/course project of 10 marks each). The weight-

age of CIE is as shown in the table below.


best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks Maximum marks

Marks

30 10 10 50


Semester end examination will be conducted for 100 marks which will be converted

into 50 marks. SEE question paper will have two compulsory questions (any 2 units)

and choice will be given in the remaining three units.

http://www.vturesource.com/vtu-question-papers/CS/2010/10CS34/Discrete-Mathematical-Structures

Second Semester

Machine Learning Techniques





Course Objectives:

To understand the basic concepts of learning and decision trees.

2. To understand the neural networks and genetic algorithms.

3. To understand the Bayesian techniques.To understand the instant

based learning.

4. To understand the analytical learning and reinforced learning

Detailed Syallabus:

Unit I 10 Hours

Introduction, Concept Learning and Decision Trees

Learning Problems – Designing Learning systems, Perspectives and Issues –

Concept Learning – Version Spaces and Candidate Elimination Algorithm –

Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic

Space Search.

Unit II 10 Hours

Neural Networks and Genetic Algorithms

Neural Network Representation – Problems – Perceptrons – Multilayer Networks and

Back Propagation Algorithms – Advanced Topics – Genetic Algorithms – Hypothesis

Space Search – Genetic Programming – Models of Evolution and Learning.

Unit III 10 Hours

Bayesian and Computational Learning

Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum description

length principle – Bayes optimal classifier – Bayesian belief network – EM algorithm –

Probably Learning – sample complexity for Finite and Infinite hypothesis spaces –

Mistake bound model

Unit IV 10 Hours

Instant based Learning and Learning set of rules

K-nearest neighbor learning – Locally weighted regression – radial basis functions – case

based reasoning – sequential covering algorithms – Learning rule sets – Learning first

order rules – Learning sets of first order rules – Induction as inverted deduction –

Inverting

Unit V 10 Hours

Analytical Learning and Reinforced Learning

Perfect Domain Theories – Explanation Based Learning – Inductive-Analytical

Approaches - FOCL Algorithm – Reinforcement Learning – Task – Q-Learning –

Temporal Difference Learning

Course Outcomes:

On Completion of the course, the students will be able to

1. Choose the learning techniques with the basic knowledge [L4].

2. Apply effectively neural networks and genetic algorithms for appropriate

applications [L3].

3. Apply Bayesian techniques and derive effectively learning rules [L3].

4. Choose and differentiate reinforcement and analytical learning techniques [L4].

Prerequisite:

1. Probability and statistics

2. Linear Algebra

3. Basic design and analysis of algorithms principles

Text Book:

1. Tom M. Mitchell, “Machine Learning”, McGraw-Hill Education (INDIAN

EDITION), 2013.

Reference Books:

1. Ethem Alpaydin, “Introduction to Machine Learning”, 2nd Edition, PHI Learning Pvt. Ltd., 2013.

2. T Hastie, R. Tibshirani, J.H.Fiedman, “The Elements of statistical learning”, Springer, 1st Edition 2001.


The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of

10 marks each and quiz/course seminar/course project of 10 marks each). The weight-

age of CIE is as shown in the table below.


best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks Maximum marks

Marks

30 10 10 50


Semester end examination will be conducted for 100 marks which will be converted

into 50 marks. SEE question paper will have two compulsory questions (any 2 units)

and choice will be given in the remaining three units.

Second Semester

Wireless Networks and Mobile Computing


Course Type: PE-B CIE Marks: 50



Course Objectives

To introduce the concepts of wireless communication.

1. To understand various propagation methods, Channel models, capacity

calculations multiple antennas and multiple user techniques used in the mobile

communication.

2. To understand CDMA, GSM, Mobile IP, Wimax.

3. To understand different Mobile OS

4. To learn various markup languages CDC, CLDC, MIDP; Programming for

CLDC, MIDlet model and security concerns.

Detailed Syllabus:

Unit I 10 Hours

Mobile Computing Architecture: Architecture for Mobile Computing, 3-tier

Architecture, Design Considerations for Mobile Computing. Wireless Networks :

Global Systems for Mobile Communication ( GSM and Short Service Messages

(SMS): GSM Architecture, Entities, Call routing in GSM, PLMN Interface, GSM

Addresses and Identities, Network Aspects in GSM, Mobility Management, GSM

Frequency allocation. Introduction to SMS, SMS Architecture, SM MT, SM MO, SMS

as Information bearer, applications, GPRS and Packet Data Network, GPRS Network

Architecture, GPRS Network Operations, Data Services in GPRS, Applications for

GPRS, Billing and Charging in GPRS, Spread Spectrum technology, IS-95, CDMA

versus GSM, Wireless Data, Third Generation Networks, Applications on 3G,

Introduction to WiMAX.

Unit II 10 Hours

Mobile Client: Moving beyond desktop, Mobile handset overview, Mobile phones

and their features, PDA, Design Constraints in applications for handheld devices.

Mobile IP: Introduction, discovery, Registration, Tunneling, Cellular IP, Mobile IP

with IPv

Unit III 10 Hours

Mobile OS and Computing Environment: Smart Client Architecture, The

Client: User Interface, Data Storage, Performance, Data Synchronization, Messaging.

The Server: Data Synchronization, Enterprise Data Source, Messaging. Mobile

Operating Systems: WinCE, Palm OS, Symbian OS, Linux and Proprietary OS

Client Development: The development process, Need analysis phase, Design

phase, Implementation and Testing phase, Deployment phase, Development Tools,

Device Emulators.

Unit IV 10 Hours

Building, Mobile Internet Applications: Thin client: Architecture, the client,

Middleware, messaging Servers, Processing a Wireless request, Wireless Applications

Protocol (WAP) Overview, Wireless Languages: Markup Languages, HDML, WML,

HTML, cHTML, XHTML, VoiceXML. 10 Hours

Unit V 10 Hours

J2ME: Introduction, CDC, CLDC, MIDP; Programming for CLDC, MIDlet model,

Provisioning, MIDlet life-cycle, Creating new application, MIDlet event handling,

GUI in MIDP, Low level GUI Components, Multimedia APIs; Communication in

MIDP, Security Considerations in MIDP.

Course Outcomes:

The student should be able to:

1. Work on state of art techniques in wireless communication [L3].

2. Explore CDMA, GSM and Mobile OS [L2].

3. Develop programs for CLDC, MIDP let model and security concerns [L3].

Prerequisite:

1. Concept of Computer Networks

Text Books:

1. Ashok Talukder, Roopa Yavagal, Hasan Ahmed: Mobile Computing,

Technology, Applications and Service Creation, 2nd Edition, Tata McGraw Hill,

2010.

2. Martyn Mallik: Mobile and Wireless Design Essentials, Wiley India, 2003

Reference Books:

1. Raj kamal: Mobile Computing, Oxford University Press, 2007.

2. Iti Saha Misra: Wireless Communications and Networks, 3G and Beyond, Tata

McGraw Hill, 2009.



marks each and quiz/course seminar/course project of 10 marks each). The weight-age of CIE

is as shown in the table below.


best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks

Maximum marks

Marks

30 10 10 50





Second Semester

Network Programming & Internet Technologies

Course Objectives:

1. To study the Transport layer.

2. To understand the basic socket address structure.

3. To know the use of socket functions.

4. To understand TCP client/server programming.

5. To study the different Internet technologies.

Detailed Syallabus:

Unit I 10 Hours

Transport Layer: Introduction, The big picture, UDP, TCP, Stream Control transmission

protocol, TCP connection establishment and termination, time-wait state, SCTP Association

establishment and terminations, port numbers, TCP port numbers and concurrent servers,

Buffer size and limitations, std Internet services protocol usage by common Internet

applications.

Unit II 10 Hours

Socket Introduction: Introduction, socket address structures, value-result arguments, Byte

ordering functions, Byte manipulation functions, inet_aton, inet_addr and inet_ntoa

functions, inet_pton and inet_ntop functions, sock_ntop and related functios, readn, written,

and readline functions.

Unit III 10 Hours

Elementary Sockets: Introduction, socket functions, connect function, bind function, listen

function, accept function fork & exec functions, concurrent servers, close function,

getsockname and getpeername functions.

Unit IV 10 Hours

TCP Client/Server Examples: Introduction, TCP Echo Server: main function, TCP Echo

Server:str_echo function, TCP Echo Client: main function, TCP Echo Client: str_cli

function, Normal startup, Normal termination, POSIX Signal Handling, SIGHE_D signals,

wait and waitpid functions, connection abort before accept returns, Termination of SIGPIPE

signal, crashing of server Host, crashing and rebooting server that shutdown of server host,





summary of TCP example, Data format

Unit V 10 Hours

Internet Technologies: Designing web page, HTML, forms, Dive into Web 2.0, Javascript

objects, XML, PHP.

Course Outcomes:


1. Write programs to interconnect computers using sockets [L6].

2. Implement the file transfer on TCP client/server architecture [L3].

3. Implement the file transfer on UDP client/server architecture [L3].

4. Design the web pages using Internet technologies like HTML and XML [L6].

Prerequisite:

1. Knowledge of ‘C’ Programming language

Text Books:

1. W. Richard Stevenson, Bill Fenner, Andrew M. Rudoff: Unix Network

Programming, volume 1, Third Edition.

2. Paul Dietel, Harvey Dietel and Abbey Dietel, Internet & World Wide Web: How to

program, Fifth Edition.

Reference Books:

1. Douglas E. Comer, David L. Stevens, “Internetworking with TCP/IP”, Vol. III: Client-

Server Programming and Applications, Linux/Posix Sockets Version: 3, 11 Sep 2000. 2. Chris Bates, “Web Programming, Building Internet applications”, Third Edition,

Wiley publications, 2013.






best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks Maximum marks

Marks

30 10 10 50





http://www.amazon.in/Douglas-E.-Comer/e/B000AQ4HHG/ref=dp_byline_cont_book_1

http://www.amazon.in/David-L.-Stevens/e/B004LQLJCM/ref=dp_byline_cont_book_2

Second Semester

Information Storage Management

Course Objectives:

1. To identify the components of managing the data centre.

2. To understand logical and physical components of a storage infrastructure.

3. To evaluate storage architectures, including storage subsystems SAN, NAS,

IPSAN and CAS.

4. To understand the business continuity, backup and recovery methods.

Detailed Syallabus:

Unit I 10 Hours

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.

Unit II 10 Hours

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 Use

Cases.

Unit III 10 Hours

Storage networking 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.





Unit IV 10 Hours

Cloud Computing

Cloud Enabling Technologies -Characteristics of Cloud Computing -Benefits of Cloud

Computing, Cloud Service Models, Cloud Deployment models, Cloud computing

Infrastructure-Cloud Challenges.

Unit V 10 Hours

Securing and Managing Storage Infracture

Information Security Framework, Storage Security Domains, Security Implementations in

Storage Networking, Monitoring the Storage Infrastructure, Storage Infrastructure

Management Activities, Storage Infrastructure Management challenges.

Course Outcomes:


1. Distinguish various data storage management systems [L4] .

2. Build storage area networks [L3].

3. Use cloud architecture for managing the data [L4].

4. Ensure security of data centres [L3].

Prerequisite:

1. Knowledge of Storage Area Networks.

Text Books

1. EMC Corporation, “Information Storage and Management”, Wiley India, 2nd Edition,

2011.

Reference Books

1. Robert Spalding, “Storage Networks: The Complete Reference”, Tata McGraw Hill,

Osborne, 2003.

2. Marc Farley, “Building Storage Networks”, Tata McGraw Hill, Osborne, 2nd Edition,

2003. 3. Meeta Gupta, “Storage Area Network Fundamentals”, Pearson Education Limited,

2002.






best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks

Maximum marks

Marks

30 10 10 50





.

Second Semester

Web Security





Course Objectives

1. To understand necessity for securing web applications

2. To know different risks to web applications

3. To take the steps required to mitigate those risks

Detailed Syallabus:

Unit I 10 Hours

Introduction:

The Web Security Landscape, Architecture of the World Wide Web, Cryptography basics,

Cryptography and the web, Understanding SSL and TLS

Digital Identification: Passwords, Biometrics and Digital Signatures.

Unit II 10 Hours

Digital Certificates, CAs and PKI: Web's war on privacy, privacy protecting techniques,

privacy protecting Technologies.

Unit III 10 Hours

Web Server Security:

Physical security for servers, Host security for servers, securing web applications.

Unit IV 10 Hours

Web Server Security:

Deploying SSL server certificates, securing your web service, computer crime

Security for content providers: Controlling access to web content, Client-side digital

certificates, code signing and Microsoft's Authenticode.

Unit V 10 Hours

Security for content providers:

Pornography, Filtering software, Censorship, privacy policies, legislation, P3P, Digital

Payments, Intellectual property and actionable content.

Course Outcomes


1. To detect and solve common web application security vulnerabilities [L3].

Prerequisite:

1. Knowledge of Network Security.

Text Book

1. Web Security, Privacy and Commerce, Simson Garfinkel, Gene Spafford, 2nd Edition, O’REILLY, 2002

Reference Book:

1. Dafydd Stuttard, “The Web Application Hacker’s Handbook”, Wiley India Pvt. Ltd.






best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks Maximum marks

Marks

30 10 10 50





Second Semester

Machine Learning Laboratory





Course Objectives:

1. To acquire a basic knowledge about the key algorithms and theory that forms the

foundation of machine learning and computational intelligence.

2. To achieve a practical knowledge of machine learning algorithms and methods.

LABORATORY WORK

(The following tasks can be implemented in a language of your choice or any tools

available)

1) Show how the CANDIDATE – ELIMINATION algorithm. Show how it is used

to learn from training examples and hypothesize new instances in Version Space.

2) Show how the FIND–S algorithm. Show how it can be used to classify new instances

of target concepts. Run the experiments to deduce instances and hypothesis consistently.

3) Demonstrate the use of ID3 algorithm for learning Boolean-valued functions for

classifying the training examples by searching through the space of a decision tree.

4) Demonstrate the back-propagation algorithm for learning the task of classification

involving applications like face-recognition.

5) Show the application of Naïve Bayes alogorithm for learning and classifying text

documents.

6) Demonstrate the use K-nearest neighbor algorithm for unsupervised learning task with the

help of a suitable example.

7) Show the use of support-vector machine for a linear classification problem of suitable

application.

The students are required to carry out a mini project based on the topics that they have

learnt.

Course Outcomes:

On Completion of the course, the students will be able to

1. Demonstrate the the use of machine learning tools such as Weka ,R and matlab.[L2]

2. Identify and apply the appropriate machine learning technique to classification,

pattern recognition, optimization and decision problems [L1,L3].


The total marks of CIE shall be 25. The weight-age of CIE is as shown in the table below.

Component Lab

Attendance

Lab Journal Internal Lab

test

Total Marks

Max. Marks 5 10 10 25

Scheme of semester-end examination (SEE):

Semester end examination will be conducted for 50 marks which will be converted to into 25

marks. The students have to execute one of the given lists of experiments for 25 marks and

demonstrate the working of the mini-project for another 25 marks.