msc cs&is syllabus

Master of Science in Computer Science and Information Security

Cochin University of Science and Technology

Indian Institute of Information Technology and Management – Kerala (IIITM-K)

Technopark www.iiitmk.ac.in

Thiruvananthapuram-695 581

Contents 1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . ….……………….... 3

1.1 Motivation…….. . . . . . . . . . . . . . . . . . . ……………. 4 1.1 Aim and Objective . . . . . . . . . . . . . . . . . . ..….. …… 4

2 Regulations. . . . . . . . . . . . . . . . . . . . . . . . …………………….. 5 2.1 Course Description . . . . . . . . . . . . . . . . . . . ………… 5 2.2 Salient Features . . . . . . . . . . . . . . . . . . . . ………….. 5 2.3 Eligibility . . . . . . . . . . . . . . . . . . . . . . . .……. … 6

2.4 Admissions . . . . . . . . . . . . . . . . . . . . . . . ……………. 6 2.5 Assessment, Evaluation and Grading System. ……… 6 2.6 Total Credit Requirements . . . . . . . . . . . . . .. ………. 7

3 Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . … …… ……. 7 3.1 Core Courses . . . . . . . . . . . . . . . . . . . . . . …………… 7 3.2 Elective Courses . . . . . . . . . . . . . . . . . . . . . …………. 8 3.3 Mini Projects… . . . . . . .. ……………………………….8 3.4 Project/Internship . . . . . . . . . . . . . . . . . . . . ………….. 9

4 Semester-wise Breakup of Courses for 2 years. . …………..... 10

5 Syllabus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . …………………. 11 5.1 Core Courses . . . . . . . . . . . . . . . . . . . . . . …………… 12 5.2 Electives . . . . . . . . . . . . . . . . . . . . . . . . . . …………… 23

1. Preamble

This document details the proposal to launch a Master of Science programme in Computer Science and Information Security by the Indian Institute of Information Technology and Management - Kerala (IIITM-K) and to get affiliated by Cochin University of Science and Technology (CUSAT). IIITM-K is an autonomous institution set up by the Government of Kerala in 2000 with a mission to become an institution of excellence in education, research, development, and training in Information Technology, Management and aligned areas. In the memorandum of Association of Indian Institute of Information Technology and Management-Kerala, dated 26-08-2000, the main objective of the institute is To conduct various educational and training programmes in Information Technology and

Management in full time as well as part time, without the objective of making profit. To emerge as a globally recognized, specialized institution for higher learning in

Information Technology, Management and related fields of study and strive to establish Kerala as an international centre of Excellence, without the objective of making profit.

To establish, administer and manage institutions, training/study centres, documentation

centres, infrastructure for research and development and any other facility required for a centre for advanced learning in Information Technology Management Entrepreneurship development and allied fields, at one or more places without the objective of making profit.

To conduct educational, research and training programmes in the field of Information

Technology, Management, Entrepreneurship development and allied fields and award degrees, fellowships, diplomas and certificates to the participants of such programs, without the objective of making profit.

To become the primary institution to set the standards of Information Technology

education and training in Kerala, without the objective of making profit. To offer consultancy, guidance to the Government, to State and Central Public Sector

Undertakings and other organizations in their drive for computerization, to train personnel involved in such activities and to help the Government formulate various policies, schemes, and projects for the promotion of wide spread application of information Technology in all walks of life, without the objective of making profits.

To act as an effective interface between industries, academic and scientific institutions

with a view to foster innovative technologies develop intellectual property, aid in patent registration and assist their commercialization, without objective of making profit.

1.1 Motivation The handling of information through electronic means is a defining technology of our age. Enormous volumes of information are routinely stored, processed and transmitted worldwide – indeed, most aspects of our daily lives would come to a halt if the information infrastructure fail. The field of Information Security, namely the study of measures and countermeasures to real and serious security threats to information, has grown very rapidly in the recent years. The subject embraces technologies such as cryptography, computer security, network security, digital forensics and fraud detection, as well management of security and trade-offs while implementing information security.

This MSc programme provides students a systematic understanding and critical awareness of the current threats to the security of electronic information and the measures available to counteract them. It is designed to introduce all technical aspects of Information Security and is intended as a foundation, or a building block, for a career in the field

1.2 Aim and Objective To impart theoretical knowledge that underpins the various areas of Computer Science

and Information Security To impart sound knowledge in Science, Technology and Management related to

Information Security and their applications in relevant fields with the latest technologies. Build a pool of technically qualified manpower to build a knowledge society. To cater the needs of government, industry and scientific organizations in the Computer

Science and Information Security areas. Develops professionals and leaders of high caliber imbued with values of

entrepreneurship, ethics and social responsibility. To motivate for research in Computer Science and Information Security. To train computer scientists who can work on real life challenging problems.

2 Regulations 2.1 Course Description Master of Science in Computer Science and Information Security will be a flagship programme offered by the Indian Institute of Information Technology and Management-Kerala, aims at offering a high standard curriculum in Computer Science and Information Security. The programme focuses on a broad grasp of foundations in Computer Science and Information Security, deep understanding of the area of specialization, an innovative ability to solve new problems, and a capacity to learn continually and interact with trans-disciplinary groups. The technology enhanced e-learning methodologies with web based course management system and on-line learning system enriches the programme, allow to broaden their horizons. The duration of the programme is 2 years and the courses are carefully designed to attain technical aspects that enable the students to grow into competent information security professionals. There are 9 core courses of 3 credits each spread across the first 3 semesters accumulating 27 credits. The students are required to do a minor project of 2 credits each during the second and third semesters accumulating 4 credits, a lab of 1 or 2 credits during the first three semesters accumulating 5 credits. The students are also required to take 1 elective course during the first semester and two elective courses during second semester and three elective courses during the third semester of 3 credits each accumulating 18 credits. The 4th semester is for project/internship of 18 credits. Students are required to undergo an industry or research oriented project in any leading IT or R &D organizations. The total requirement for the programme is 72 credits.

2.2 Salient Features 1.Students are selected through an entrance examination and an interview. 2.Students are provided with high-end network and software services, e-learning technologies, and multimedia facilities 3.Courses at basic and advanced levels with cutting edge technologies 4.Highly qualified faculty actively engaged in teaching and research. Also visiting faculty from leading institutes and industries. 5.Support of Digital Library with good collection of e-journals, e-books, online reports and other digital materials. 6.Multimedia Digital Library with course videos available at any time. 7.Teamwork and students community and collaboration group enable healthy exchange of information. 8.Students can participate in ongoing research and technology development, live projects and networks. 9.The technology enhanced learning methodology and e-learning framework allows students to learn at anytime in their own pace. 10.Situated in Technopark, India's largest IT park that hosts over 150 IT companies allows students to interact with techies and get in touch with current technologies and developments.

2.3 Eligibility Entry-level requirement is a Bachelor's degree in any branch of Engineering/ Technology with minimum score of 60 percentage marks or CPI/CGPA of above 6.5 in 10 points, in the qualifying examination. Changed as per Academic Council Resolution – meeting held on 27-03-2013

“Bachelor’s Degree in any branch of Engineering/Technology OR

Bachelor’s Degree in Computer Science/Computer Applications/

Information Technology OR Bachelor’s Degree in Mathematics

/Physics/Chemistry/Statistics; with Mathematics as a subject of study with

minimum score of 60% of marks or CGPA of 6.5 in 10 point scale in the

qualifying examination”.

2.4 Admissions Students are selected through an All India entrance examination and an interview under the supervision of CUSAT.. Reservation of seats for SC/ST, OBC etc. is applicable as per

CUSAT rules. The final selection of the candidates is done through an interview from the short listed candidates of written test. The total intake of the students is 40.

2.5 Assessment, Evaluation and Grading System There will be 40% for internal examination and 60% external examination marks for all courses except the minor projects. The minor projects will have only internal evaluations. There will be a continuous assessment for classroom performance, lab exercises, seminars and discussions. The evaluation scheme for each semester has internal assessment, End Semester Examinations and lab examinations. All practical examinations will be internally evaluated. The Question paper for the end semester external examination shall have Part A having 15 Questions of 2 marks each and Part B having 5 Questions of 6 marks each with a total of 60 marks out this 60, the minimum score required to secure a pass shall be 45%. There shall be a maximum of 40 marks for internal assessments. In order to secure a pass in any subject the candidate has to score an aggregate of 50% of the total of end semester examination and internal assessment marks. The evaluation of a student’s performance at the end of the semester results in a grade, and a grade card will be issued on completion of each semester. The grade pattern is given below:

Marks Range Grade Weightage 90% and above S-Outstanding 10

80-89 A-Excellent 9

70-79 B- Very Good 8 60-69 C-Good 7 50-59 D- Satisfactory 6

Below 50% F-Failed 0

There will be a Grade W and I, where W is withheld and I is incomplete. The Performance Index (PI) of a student over a set of credited courses c1 , ... cn is a measure of the student's average performance over that set of courses. PI is calculated as the average grade point over the set of credited courses weighted by the number of credits for each course.

Overall performance at the end of the semester will be indicated by Semester Performance Index

(SPI) and is calculated as follows:

SPI = G1C1 + G2C2 + G3C3 + ……..+GnCn C1+C2+C3+…………………...+Cn.

where ‘G’ refers to the grade weightage and ‘C’ refers to the credit value of corresponding course

undergone by the student.

At the end of the final semester the Cumulative Performance Index (CPI) will also be calculated

based on the above formula.

2.6 Total Credit Requirements

Each course has assigned a fixed number of credits. In addition there are 18 credits for research and internship projects. The student should have accumulated a total of at least 72 credits. The minimum grade for attaining the degree is 6.5.

3 COURSES The curriculum comprises of core courses, elective courses, mini projects and internship/project. The credit requirements for the degree are summarized below.

Requirement Credits Core Courses 27 Elective Courses 18 Mini Projects 04 Internship/Project 18 Total Credits 72

3.1 Core Courses The student is required to earn 27 credits from the following 9 core courses:

1. ISMS2101 Number Theory and Algebra 2. ISMS2102 Computer Networks, Security and Cyber Crimes 3. ISMS2103 Computer Architecture and Organization 4. ISMS2104 Object Oriented Programming in JAVA 5. ISMS2201 Cryptography 6. ISMS2202 Operating Systems 7. ISMS2203 Data Structures and Algorithms 8. ISMS2301 Database Management Systems and Security 9. ISMS2302 Information Systems Control and Audit

3.2 Elective Courses The elective courses are offered in the 1st , 2nd and the 3rd semesters. A student is required to earn 18 credits in 6 elective courses from the following list of 24 elective courses:

1. ISMS2001 Advanced Topics in Cryptography 2. ISMS2002 Cryptanalysis 3. ISMS2003 Secure Internet Programming 4. ISMS2004 Pattern Recognition for Computer Security 5. ISMS2005 Biometrics for Security 6. ISMS2006 Steganography, Digital Watermarking and DRM 7. ISMS2007 Theory of Computation 8. ISMS2008 Statistical Methods 9. ISMS2009 Scientific Computing 10. ISMS2010 High Performance Computing 11. ISMS2011 Digital Signal Processing 12. ISMS2012 Artificial Intelligence 13. ISMS2013 Software Engineering 14. ISMS2014 Soft Computing 15. ISMS2015 Web Technology 16. ISMS2016 Object Oriented Analysis and Design 17. ISMS2017 Principles of Management 18. ISMS2018 Computational Linguistics

19. ISMS2019 Embedded Systems 20. ISMS2020 Security in Distributed Environments 21. ISMS2021 Data Analytics 22. ISMS2022 Digital Image Processing and Pattern Recognition 23. ISMS2023 Autonomic and Context-Aware Computing 24. ISMS2024 Cryptography Standards

3.3 Mini Projects A student is required to do one mini project related to Information Security or Computer Science each during the Semester 2 and Semester 3 independently under the guidance of any faculty member of the institute. This facilitates the student to get familiarize with the latest research and development trends in the field. At the end of the semester the student is required to submit a report of the mini project and give an oral presentation of the mini project carried out by him/her. The project report and the oral presentation will be evaluated by a 3 member committee comprising of the faculty members of the institute including the project guide. The project report and the oral presentation carries 25 marks each. There will not be any external evaluation for the mini projects. The mini-project in all semesters carry 2 credit each. The following are the mini-projects:

1. ISMS2206 Mini Project 1 (Semester 2) 2. ISMS2306 Mini Project 2 (Semester 3)

3.4 Core Labs A student is required to do two labs in the first semester and one in each of the second and third semester. The lab report and lab examination carries 25 marks each. There will not be any external evaluation for the labs. The lab in all the semesters carry one or two credits each.

1. ISMS2106 Java Programming (Semester 1) 2. ISMS2107 Network Security Lab (Semester 1) 3. ISMS2207 Cryptology Lab (Semester 2) 4. ISMS2307 Cyber Forensics Lab (Semester 3)

3.5 Project/Internship A student is required to do a project related to Information Security during the Semester 4, independently under the guidance of any faculty member of the institute or as an internship project in an industry or any reputed academic/research institute. If a student is opting for an internship project in an industry or any other reputed academic/research institute, he is required to have an internal guide from the institute. The project/internship aims to provide the student an opportunity to participate and work in a major research/development activity. Typically, the industry internship helps the student to learn about work culture, business processes, technologies, marketing strategies, etc. At the end of the semester the student is required to submit a report of the project/internship and give an oral presentation of the project/internship carried out by him/her. The project report and the oral presentation will be evaluated by both an internal committee comprising of the faculty members of the institute including the project guide as well as an external committee constituted by the university. The internal and external evaluation of the project report and the oral presentation carries 250 marks each. The project/internship carries 18 credits. The projects/internship is:

1. ISMS2401 Project/Internship

Semester-wise Breakup of Courses for 2 Years

For graduation, the student must satisfy all the requirements as per university rules.

Semester I

No C.Code Course Title Credits Lect Lab IE UE Tot

1 ISMS2101 Number Theory and Algebra

3 3 0 40 60 100

2 ISMS2102

Computer Networks, Security and Cyber Crimes

3 3 0 40 60 100

3 ISMS2103 Computer Architecture and Organization

3 3 0 40 60 100

4 ISMS2104 Object Oriented Programming in JAVA

3 3 0 40 60 100

5 ISMS2EL1 Elective 1 3 3 0 40 60 100

6 ISMS2106 Java Programming Lab 1 0 3 50 * 50

7 ISMS2107 Network Security Lab 2 0 6 50 * 50

* - Total for Semester I 18 15 9 300 300 600

IE- Internal Examination UE - University Examination

Semester II


1 ISMS2201 Cryptography 3 3 0 40 60 100 2 ISMS2202 Operating Systems 3 3 0 40 60 100

3 ISMS2203 Data Structures and Algorithms

3 3 0 40 60 100

4 ISMS2EL2 Elective 2 3 3 0 40 60 100

5 ISMS2EL3 Elective 3 3 3 0 40 60 100

6 ISMS2206 Mini Project 1 2 0 0 50 * 50

7 ISMS2207 Cryptology Lab 1 0 3 50 * 50

* - Total for Semester II 18 15 3 300 300 600

IE - Internal Examination UE - University Examination

Semester III


1 ISMS2301 Database Management Systems and Security

3 3 0 40 60 100

2 ISMS2302 Information Systems Control and Audit

3 3 0 40 60 100

3 ISMS2EL4 Elective 4 3 3 0 40 60 100

4 ISMS2EL5 Elective 5 3 3 0 40 60 100

5 ISMS2EL6 Elective 6 3 3 0 40 60 100

6 ISMS2306 Mini Project 2 2 0 0 50 * 50

7 ISMS2307 Cyber Forensics Lab 1 0 3 50 * 50

* - Total for Semester III 18 15 3 300 300 600

Semester IV

No C.Code Course Title Marks

IE UE Total

1 ISMS2401 Project/Internship 250 250 500

5. SYLLABUS

Core Courses

1. ISMS2101 Number Theory and Algebra 2. ISMS2102 Computer Networks, Security and Cyber Crimes 3. ISMS2103 Computer Architecture and Organization 4. ISMS2104 Object Oriented Programming in JAVA 5. ISMS2201 Cryptography 6. ISMS2202 Operating Systems 7. ISMS2203 Data Structures and Algorithms 8. ISMS2301 Database Management Systems and Security 9. ISMS2302 Information Systems Control and Audit

Core Labs

1. ISMS2106 Java Programming Lab 2. ISMS2107 Network Security Lab 3. ISMS2207 Cryptology Lab 4. ISMS2307 Cyber Forensics Lab

Elective Courses

1. ISMS2001 Advanced Topics in Cryptography 2. ISMS2002 Cryptanalysis 3. ISMS2003 Secure Internet Programming 4. ISMS2004 Pattern Recognition for Computer Security 5. ISMS2005 Biometrics for Security 6. ISMS2006 Steganography, Digital Watermarking and DRM 7. ISMS2007 Theory of Computation 8. ISMS2008 Statistical Methods 9. ISMS2009 Scientific Computing 10. ISMS2010 High Performance Computing 11. ISMS2011 Digital Signal Processing 12. ISMS2012 Artificial Intelligence 13. ISMS2013 Software Engineering 14. ISMS2014 Soft Computing 15. ISMS2015 Web Technology 16. ISMS2016 Object Oriented Analysis and Design 17. ISMS2017 Principles of Management 18. ISMS2018 Computational Linguistics 19. ISMS2019 Embedded Systems 20. ISMS2020 Security in Distributed Environments 21. ISMS2021 Data Analytics 22. ISMS2022 Digital Image Processing and Pattern Recognition 23. ISMS2023 Autonomic and Context-Aware Computing 24. ISMS2024 Cryptography Standards

5.1 Core Courses

1. ISMS2101: Number Theory and Algebra

Core/Elective: Core, Semester: I, Credits: 3

Module 1 Basic Properties of the integers: Divisibility and primality, Congruences: -Definitions and basic properties, Solving linear congruences, Residue classes, Euler’s phi function, Fermat’s little theorem, Arithmetic functions and Mobius inversion.

Module 2

Computing with large integers:-Asymptotic notation, Basic integer arithmetic, Computing in Zn, Faster integer arithmetic; Euclid’s algorithm -The basic Euclidean algorithm, The extended Euclidean algorithm, Computing modular inverses and Chinese remaindering, Speeding up algorithms via modular computation, Rational reconstruction and applications. Module 3

Quadratic residues and quadratic reciprocity:-Quadratic residues, The Legendre symbol, The Jacobi symbol.

Module 4

Groups, Definitions, basic properties, and examples, Subgroups, Cosets and quotient groups, Group homomorphisms and isomorphisms, Cyclic groups Vector spaces, Definitions, basic properties, Linear independence and bases, dimension. Module 5 Finite fields, The existence of finite fields, The subfield structure and uniqueness of finite fields

References:

1. V. Shoup, A computational introduction to number theory and algebra, Cambridge University Press, 2nd Edition, 2005.

2. Neal Koblitz,. A Course in Number Theory and Cryptography, Springer Verlag (low price edition), 2nd Edition, 1994

3. I N Herstein, Topics in Algebra, Wiley India, 2nd Edition, 2006 4. M. Mignotte, Mathematics for computer algebra, Springer-Verlag, 1992. 5. Niven, H.S. Zuckerman and H.L. Montgomery, An introduction to the theory of

numbers, John Wiley, 5th Edition, 1991. 6. Ireland and Rosen , A Classical Introduction to Modern Number Theory, Springer

Verlag, 2nd Edition, 1990, 7. H. Cohen, A course in computational algebraic number theory, Springer-Verlag, 1st

Edition, 1993. 8. M. Artin, Algebra, Pearson; ; 2 edition (2011)

2. ISMS2102 Computer Networks, Security and Cyber Crimes


Module 1

Samuel Gregory

Samuel Gregory

Samuel Gregory

Samuel Gregory

Samuel Gregory

Samuel Gregory

Samuel Gregory

Samuel Gregory

Samuel Gregory

Introduction: Layering concept - OSI reference model - Components of a LAN - Physical layer. Data Link Layer: Services provided – Error Control - Medium Access Control Sub layer - Flow control protocols - LAN Protocols - IEEE 802 LANs Module 2

Network Layer: Services provided - Routing Algorithms – Congestion control algorithms - Internetworking issues Transport Layer: Design Issues - Connection management - TCP/IP protocol suite. Other Layers: Session layer - Presentation and Application layers - Higher level protocols – Programming Module 3

Kerberos – X509 Authentication service – IP security Architecture – Secure socket layer – Electronic mail security – Pretty Good privacy – S/MIME – secure Electronic Transactions –Trusted Intermediaries - Public Key infrastructures, Certification authorities and key distribution centers, Firewalls - Packet filters, Application level gateways, Encrypted tunnels, Web security, VPN Security, trust and reputation as soft security mechanisms - Security mechanisms in JAVA platform – Applet security.

Module 4

Types of computer crime, history, surveys, global connections, Data Protection, Criminal Damage, Software Piracy, Forgery, PC misuse and forensics.

System Security: Intruders – Intrusion Detection, Password management, Network Management: Monitoring and Control – SNMP V2 V3, RMON RMON2.

Module 5

Malwares, Computer viruses, denial of service attacks and Trojan horses, Network Crimes, Hacking methodologies and its history, social engineering, Password Cracking, Insecure Network connection, Malicious Code, Programming Bugs, Cyber crime and Cyber terrorism.

References

1. Doublas E. Comer, Internetworking with TCP/IP Vol.1: Principles, Protocols, and Architecture, Prentice Hall; 5 edition (July 10, 2005)

2. Andrew Tanenbaum, Computer Networks, Prentice Hall 3rd and 4th Edition, 2003 3. William Stallings, Cryptography and Network Security Principles and Practice,

Fifth, Edition, Prentice-hall, 2010. 4. Dorothy E. Denning, Information Warfare and Security, Addison-Wesley, 1999. 5. Alberto Leon-Garcia and Indra Widjaja, Communication Networks: Fundamental

Concepts and Key Architectures, McGraw-Hill, 2 edition (May 2003) 6. Matt Bishop, Computer Security, Art and Science, Pearson Education, 2003. 7. Proctor Paul, The Practical Intrusion Detection Handbook, Third Edition, Prentice-

Hall, Englewood Cliffs, 2001. 8. Erbschloe Michael, Information Warfare: How To Survive Cyber Attacks, Tata

McGraw-Hill, New Delhi, 2001. 9. Bruce, Schneier, “Applied Cryptography”, Wiley; 2nd edition (October 18, 1996) 10. Man Young Rhee, “Internet Security”, Wiley , 2003. 11. C. P. Pfleeger, A. L. Pfleeger, D. N. Shah, Security in Computing, Pearson, 4th

Edition, 2006

3. ISMS2103 Computer Architecture and Organization

Core/Elective: Core, Semester: I, Credits: 3 Module 1

Basic Structure of Computer Hardware and Software: Basic Computer Structures, Bus Structure, Control Unit Von Neumann Architecture. Instruction Sets and Addressing Methods: Instruction Sets, Addressing Modes, Data Transfer and Manipulation Program Control, Assembly Language, Subroutines, RISC and CISC. Module 2

Programming the Basic Computer: Machine Language, Assembler, I/O Programming. Processing Unit: General Register Organization, Bus Structure, Design of Control Unit, Hardwired Controllers, Micro-programmed Controllers, Control Memory, Address Sequencing, Micro program Example Arithmetic Unit: Addition and Subtraction, Design of Fast Adders, Multiplication Algorithms, Division Algorithms, Floating-Point Arithmetic Operations, Fast Adders. Module 3

Input-Output Organization: Peripheral Devices, I/O Interface, I/O Hardware, Asynchronous Data Transfer, Modes of Transfer, Interrupts, Direct Memory Access, Serial Communication Memory Organization: Memory Hierarchy, Main Memory, Auxiliary Memory, Associative Memory, Cache Memory, Virtual Memory, Memory management. Module 4 Pipelining: Instruction Pipeline, RISC Pipeline, Data Dependency Module 5

Advanced Computer Architecture: Parallel Processing, Characteristics of Multiprocessors, Interconnection Structures, Interprocessor Arbitration, Interprocessor Communication and Synchronization, Cache Coherence, Vector/Array Processing. References

1. John L. Hennesy, David A. Patterson Computer Organization and Design: The Hardware / Software Interface (Third Edition), Morgan Kaufmann, 2004

2. William Stallings, Computer Organization and Architecture: Designing for Performance (Seventh Edition), Prentice-Hall India, 2006

3. Carl Hamacher, Zvonko Vranesic and Safwat Zaky, Computer Organization (Fifth Edition), McGraw Hill, 2002

4. ISMS2104 Object Oriented Programming in JAVA

Core/Elective: Core, Semester: I, Credits: 3 Module 1

Object Oriented Paradigm and JAVA overview: Object oriented Concepts: Introduction to OOPS, Abstraction, Encapsulation, Objects and Classes, Constructors Inheritance, Polymorphism, Abstract Classes, Interfaces, Introduction to Java, JVM, Primitive data types, Control Statements, Methods, Classes Introduction to Java Compilers and Lab Module 2

JAVA statements: Java's selection statements, iteration statements, jump statements, Introduction to classes: Class fundamentals, declaring object reference variable, Introducing methods, constructors, the key word, garbage collection, the finalize (), method. Methods and Classes Overloading methods, using objects as parameters Module 3

Java Arrays, Utilities and Packages: Java Arrays, Wrapper Classes, Java IO, Inheritance, Super class, Polymorphism, java Packages, class libraries, Interfaces, Exception Handling, JAVA Strings

Module 4 Multithreading and JAVA Networking: The Java thread model, the main thread, creating thread, creating multiple thread, using is alive () and join (). Thread priorities, synchronization, Inter thread communications, suspending resuming and stopping thread using multithreading Networking: Networking basics, Java and the Internet Address, TCP/IP client Sockets, URL, URL connection, TCP/IP server Sockets The Applet Class Module 5

Java 2 Security Model, SSL, SSH, Messaging, Synchronous and Asynchronous, Java Encryption, cryptography algorithms: secret key, public key, and hash functions. SHA algorithms, Message Digest algorithm, Message Authentication Code, JCE-Java Cryptography Extension, Digital Signatures. References

1. Patrick Naughton, Helbert Schildt, "The Complete Reference JAVA 2", Tata McGraw-Hill, fifth edition, 2002

2. C. Thomas Wu, "An Introduction to Object-Orinted Programming with java”

MCGraw-Hill Science/Engineering/Math; 4 edition (January 13, 2005)

3. Jonathan and Knudsen, Java Cryptography, Orelly publication, 1998

4. Bruce Eckel, Chuck Allison, "Thinking in Java", Edition 4, Prentice Hall, 2006

5. Cay Horstmann, Computing Concepts With JAVA 2 Essentials, 2ND ED, Published by Wiley-India, 2006

6. Jalil Feghhi, Peter Williams, Digital Certificates: Applied Internet Security, Addison-

Wesley,1998

5. ISMS2201 Cryptography

Core/Elective: Core, Semester: II, Credits: 3

Module 1 Classical Cryptography: – Some Simple Cryptosystems, Shift Cipher, Substitution Cipher,Affine Cipher, Vigenere Cipher,Hill Cipher, Permutation Cipher, Stream Ciphers. Cryptanalysis of the Affine–Hill and LFSR Stream Cipher, Product Cryptosystems. Module 2 Block Ciphers: –Substitution Permutation Networks, Linear Cryptanalysis, Differential Cryptanalysis, Data Encryption Standard (DES), Advanced Encryption Standard (AES). Module 3 Cryptographic Hash Functions: Hash Functions and Data integrity, Security of Hash Functions, iterated hash functions- MD5, SHA 1. Module 4 Public Key Cryptography, RSA Crypto System, Discrete Log,, Diffie Hellman Key Exchange, ElGamal Cryptosystem, Rabin Cryptosystem, Elliptic Curve Cryptosystem Module 5 Signature Schemes: RSA Signature, The ElGamal Signature Scheme, The Digital Signature Standard The Shamir Secret Sharing Scheme, Zero-knowledge protocols References

1. Douglas R. Stinson, Cryptography Theory and Practice, CRC-Press; 3rd edition, 2005 2. Neal Koblitz,. A Course in Number Theory and Cryptography, Springer Verlag (low

price edition), 2nd Edition, 1994. 3. H. Deffs & H. Knebl , Introduction to Cryptography, Springer – Verlag, 2002. 4. Alfred J. Menezes, Paul C. van Oorschot and Scott A. Vanstone, Handbook of

Applied Cryptography, CRC Press, 1996. 5. William Stallings, Cryptography and Network Security Principles and Practice, Fifth

Edition, Pearson , 2011

6. ISMS2202 Operating Systems


Module 1 Basic Concepts: Historical Perspective and Evolution - Computer System Operation - Operating System Components - Operating System Services - Operating System Structure - Operating System Functions - System Calls - System Programs - Interrupts - Operating Systems view of Computer. Process Management: Process Scheduling - Operations on Processes - Cooperating Processes - Basic Concepts of CPU Scheduling - Scheduling Criteria - Scheduling Algorithms - Multiple-Processor Scheduling - Real-Time Scheduling - Inter-Process Communication - Communication in Client Server Systems. Thread: Benefits of Threads - User and Kernel Threads - Multithreading Modules - Linux Threads

Module 2 Process Synchronization: Race Conditions - The Critical-Section Problem - Mutual Exclusion - Semaphores - Monitors. Deadlock: Deadlock Characterization - Methods for Handling Deadlocks - Deadlock Prevention - Deadlock Avoidance - Deadlock Detection - Recovery from Deadlock - Two Phase Locking Module 3 Memory Management: Memory Management Functions - Memory Architecture Evolution - Swapping - Multiprogramming with Partitions - Contiguous Memory Allocation - Paging - Design issues for paging systems - Segmentation. Virtual Memory: Demand Paging - Page Replacement - Allocation of Frames - Thrashing - Memory Management with bit maps, linked lists - Buddy Systems Module 4 File System: File Concepts - Access Methods - Directory Structure - Security and Protection - File-System Structure - Allocation Methods - Free-Space Management –Directory Implementation - Efficiency and Performance - Disk Scheduling - Log-Structured File System. I/O System: Principles of I/O hardware - Principles of I/O software - I/O Requests Handling - Transforming I/O to Hardware Operations - Kernel I/O Subsystem – Performance Module 5 Distributed Systems: Design Issues - Sockets - Remote Procedure Calls - Remote Method Invocation - Object Registration - Event Ordering - Synchronization - Mutual Exclusion - Deadlock Handling – Election Algorithms. Protection: Domain of Protection - Access Matrix - Revocation of Access Rights - Language-Based Protection. Security: Authentication - Program Threats - System Threats - Threat Monitoring - Encryption - Computer-Security Classifications References

1. Avi Silberschatz and Peter Galvin, and Greg Gagne, Applied Operating System Concepts, WSE WILEY, 7th Edition 2007

2. Gary Nutt, Operating Systems: A Modern Perspective, Pearson Education Asia 2nd

Edition 2004

3. Andrew S. Tanenbaum Albert S.Woodhull, Operating Systems Design and Implementation, Second Edition, 2006

7. ISMS2203 Data Structures and Algorithms


Module 1 Introduction to ADT and Algorithms: Principles of DSA, ADT, computational problem, algorithm notion, time complexity, space complexity, asymptotic analysis, analysis of algorithms, design of algorithms, data, abstract data type, procedural abstraction, worst case complexity, Big-Oh notation, incremental design. Module 2 Stack and Queues: Introduction to stack, basic operations, implementation using array and linked list, computational problems relating to stack, parenthesis matching, expression representation using Polish and reverse Polish notations, evaluation of expression using stack, introduction to queues, basic operations, implementation

Module 3 Lists and Linked List: Lists in ADT, List implementation in Stack and Queue, Linked list, Insert, delete operations, doubly linked list, implementation, ADT and applications, INFIX and POSTFIX evaluations.

Module 4 Recursion and Heap: Closed form, recursive form, problem solving, Fibonacci series, Towers of Hanoi, celebrity problem (with and without recursion, Efficiency of Recursion Algorithm, eight Queens, Heap: Introduction, max heap, min heap, representation, complexity. Module 5 Trees, Graphs and Hashing: Binary tree, traversal in a tree, level order traversal, ADT dictionary, dictionary implementation, balanced binary search tree, binary search tree, extended binary tree, insertion, deletion, AVL trees, Fibonacci tree, B-tree, red black tree. Graph: Weighted graph, spanning tree, greedy method, Krushkals algorithm, implementation, equivalence relation, parent chasing, traversal, DFS and BFS, Hashing: open address hashing, double hashing, chaining, Different search and sort algorithms: Bubble, quick sort, merge sort-divide and conquer method, Heap sort. References

1. A.D Aho, J. E. Hopcroft and J. D. Ullman, Data Structures and Algorithms, Pearson education Asia, 1983.

2. Y. Langsam, M. J. Augenstein and A. M. Tenenbaum, Data Structures using C,

Pearson Education Asia, 2004

3. T.H. Cormen, C.E.Leiserson, R.L.Riverst and C. Stien, Introductin to algorithms, Second Edition. MIT Press and McGraw-Hill, 2001.

4. Adam Drozdek, Data Structures and Algorithms in Java, Published by Brooks/Cole,

2nd edition 2002

8. ISMS2301 Database Management Systems and Security

Core/Elective: Core, Semester: III, Credits: 3

Module 1 Introduction to Database Management Systems: Data, Information, Database, Transaction and its desired properties, File Server Model, Client Server Model, Advantages of using DBMS over conventional methods, DBMS Features, Components of DBMS, Data Abstraction, Data Independence. Module 2

Data Modeling: Logical and Physical Data Models, E-R Modeling A detailed study, Record Based Models, Relational Model An overview, Relational Concepts, Tables, Keys, Constraints, Data Integrity and Constraints, Integrity Rules, Database Objects Schema and Non-schema, Normalisation, Codds Rules. Module 3 Introduction to SQL: Introduction to SQL, SQL Features, SQL Operators, SQL Datatypes, SQL Parsing, Types of SQL Commands, Advanced Study of Structured Query Language,

Querying Data from the database, Correlated Sub-queries, Joins, Hierarchical Queries, PL/SQL A detailed study, Introduction, PL/SQL Architecture, Types of PL/SQL programs, Operators in PL/SQL, Datatypes in PL/SQL, Bind Variables, Cursors A detailed study, Functions, Stored Procedures, Triggers A detailed study Module 4 Distributed Databases: Structure and design, Distributed query processing, Recovery, Commit protocols, Concurrency controls, Deadlock handling, Shadow paging Module 5 Levels of Database Security: Human level, network/user interface, database application program, database system, operating system, and physical level. Authorization of databases: authorization, application security, SQL authorization, Multiple Access Control Policies, Oracle virtual private database, Techniques used by hackers to exploit database flaws and vulnerabilities, Web security vulnerabilities, Passwords in scripts, insider/outsider attacks, Identity Management in database systems. References

1. Abraham Silberschatz; Henry F Korth, Database System Concepts, McGraw Hill Publication, edition 6, 2010

2. Won Kim, Introduction to Object-Oriented Databases, MIT Press, 1990 3. Elmasri,Ramez; Navathe, Shamkant B, Fundamentals of Database Systems, Addison

Wesley; 5th edition (March 17, 2006) 4. Ron Ben Vatan, Implementing Database Security & Auditing, Digital Press; 1

edition (May 2, 2005) 5. Stefano Ceri; Giuseppe Pelagatti, Distributed Databases: Principles and Systems,

Universities Press, 2000 6. Jan L Harrington, Object Oriented Database Design Clearly Explained, Harcourt,

2000 7. C. P. Pfleeger, A. L. Pfleeger, D. N. Shah, Security in Computing, Pearson, 4th

Edition, 2006

9. ISMS2302 Information Systems Control and Audit

Core/Elective: Core, Semester: III, Credits: 3

Module 1 Overview of Information system Auditing, Need for control audit of computers, conducting an information system audit, top management control, system development management control, programming management control, data resource management control, security management control, operational management control, quality assurance management control Module 2 Application Control of Frameworks, Boundary controls, communication controls, processing controls, database controls, outout controls Module 3 Audit Softwares, Code Review, Test Data and Code comparison, Concurrent Auditing Techniques, Interview, Questionnaires and Control Flow Charts Module 4

Evaluating Asset safeguarding and Data Integrity, Evaluating System Effectiveness & Efficiency, Managing the Information Systems and Audit Module 5 Security policies, confidentiality policies, Assurance and Trust, building secure and trusted systems, Assurance in SDLC, Ethical issues in Computer Security References

1. Ron Weber, Information System Audit and Control, Prentice Hall (October 29, 1998) 2. Wante, Donald A, Peter PB, Auditing EDP Systems, Prentice Hall College Div; Fac

Sub edition (February 1990) 3. Gasser, Morrie, Building a Secure Computer System, Van Nostrand Reinhold (May

1988) 4. Bruno, Paul R, Skill Enhancement for EDP, Auerbach, 1996 5. Control Objectives for Information and Related Technological Framework: Rolling

Meadows, ISACA Foundation.

5.2 Core Labs

1. ISMS2106 Java Programming Lab


This is the lab session for the course ISMS2104 Object Oriented Programming in JAVA.

2. ISMS2107 Network Security Lab

Core/Elective: Core, Semester:I, Credits:2

A thorough study of packet capturing tool called WireShark.

Familiarizing Network Simulator – 2 (NS2) with suitable examples.

Simulate a wired network consisting of TCP and UDP Traffic using NS2 and then calculate their respective throughput using AWK script.

Simulate a wireless network consisting of TCP and UDP Traffic using NS2 and then calculate their respective throughput using AWK script.

Performance evaluation of different ad-hoc wireless routing protocols (DSDV, DSR, AODV) using NS2.

Detect probes or attacks; including operating system fingerprinting attempts, common gateway interface, buffer overflows, server message block probes, and stealth port scans using Snort.

Test system vulnerabilities using SATAN. Gather as much information as possible about system and network services, such finger, NFS, NIS, ftp, rexd., software bugs, poorly or improperly setup network utilities, services or network configurations.

3. ISMS2207 Cryptology Lab


Perform arbitrary precision calculations, compute factorization, perform elliptic, curve computations and perform algebraic number theory calculations using PARI/GP and GMP.

Use Open SSL to analyze various cryptographic algorithms.

Symmetric Ciphers

AES, Blowfish, Camellia, SEED, CAST-128, DES, IDEA, RC2, RC4, RC5, Triple DES

Cryptographic hash functions

MD5, MD2, SHA-1, SHA-2, RIPEMD-160, MDC-2

Public-key cryptography

RSA, DSA, Diffie-Hellman key exchange, Elliptic curve

4. ISMS2307 Cyber Forensic Lab

Core/Elective: Core, Semester: III, Credits:I

Computer Forensics, Image/Audio/Video Forensics, Email Forensics, Web Forensics Packet Sniffers, IP Traceback techniques, ICMP Traceback.

5.3 Elective Courses

1. ISMS2001 Advanced Topics in Cryptography

Core/Elective: Elective, Semester: II/III, Credits: 3 Module 1

Notions of Semantic Security (SS) and Message Indistinguishability (MI): Proof of Equivalence of SS and MI, Hard Core Predicate, Trap-door permutation, Goldwasser-Micali Encryption Goldreich-Levin Theorem: Relation between Hardcore Predicates and Trap-door permutations Module 2 Formal Notions of Attacks: Attacks under Message Indistinguishability: Chosen Plaintext Attack(IND-CPA), Chosen Ciphertext Attacks (IND-CCA1 and IND-CCA2), Attacks under Message Non-malleability: NM-CPA and NM-CCA2, Inter-relations among the attack model Module 3 Random Oracles: Provable Security and asymmetric cryptography, hash functions One-way functions: Weak and Strong one way functions Module 4 Pseudo-random Generators (PRG): Blum-Micali-Yao Construction, Construction of more powerful PRG, Relation between One-way functions and PRG, Pseudo-random Functions (PRF) Building a Pseudorandom Permutation: The Luby Rackoff Construction: Formal Definition,

Module 5 Message Authentication Codes (MACs): Formal Definition of Weak and Strong MACs, Using a PRF as a MAC, Variable length MAC Public Key Signature Schemes: Formal Definitions, Signing and Verification References

1. Hans Delfs, Helmut Knebl, "Introduction to Cryptography, Principles and Applications", Springer; 2nd edition (April 4, 2007)

2. Jonathan Katz, Yehuda Lindell, "Introduction to Modern Cryptography", Chapman and Hall/CRC; 1 edition (August 31, 2007)

3. Wenbo Mao, "Modern Cryptography, Theory and Practice", Prentice Hall; 1 edition (August 4, 2003)

4. Shaffi Goldwasser and Mihir Bellare, Lecture Notes on Cryptography, 2008. 5. O. Goldreich, Foundations of Cryptography, Part 1 and Part 2 , Cambridge University

Press; 1 edition (January 18, 2007) 2. ISMS2002 Cryptanalysis

Core/Elective: Elective, Semester: II/III, Credits: 3

Module 1

Problem of malleability using encryption with textbook RSA, ElGamal and the one-time pad Some criteria for and different types of cyclic groups used in cryptography: (prime order subgroups of the) multiplicative group of finite fields, elliptic curves. Module 2 Linear Cryptanalysis (Matsui), Differential cryptanalysis (Biham), Hash function attacks (Damgaard's MD4 attack, Wang's attack on MD4, MD5, SHA-1), Attack on SHA-0, Multi-collision attacks (both by Joux), Time Memory trade-off (Hellman), Module 3 Attacks against Discrete log problem (baby step giant step), Pollard's rho, index calculus, Pohling-Hellman), Attacks against RSA, small exponent attacks (Shamir, Coppersmith), Dixon's algorithm and the quadratic sieve for factoring integers, Pollard's p-1 method. Module 4 Introduction to side channels, Fault attacks, Cache timing, Memory remanence; Simple Power Analysis (SPA), Differential Power Analysis (DPA), Timing attacks; countermeasures against side-channel attacks. Module 5 Meet in the middle attack (against DES, Sasaki's attacks for preimage of hash functions), Rebound attack (Rechberger, Peyrin et al), Algebraic attacks, Rectangle attack, Related key attack, Biclique analysis, Security requirements for (password-authenticated) two-party key establishment; Lowe's attack on the Needham-Schroeder public-key protocol References

1. Samuel S. Wagstaff , Cryptanalysis of number theoretic ciphers, Chapman &

Hall/CRC, 2003

2. C. Swenson, Modern Cryptanalysis: Techniques for Advanced Code Breaking, Wiley, 2008

3. Alfred J. Menezes, Paul C. van Oorschot and Scott A. Vanstone, Handbook of Applied Cryptography, CRC Press, 1996.

3. ISMS2003 Secure Internet Programming


Module 1 Building Internet Applications (BIA): Protocol design, distributed computing models; client server, peer-peer, broadcast, fat client, thin client. Technologies including dynamic Web pages, Java network programming. Module 2 Internet Security Issues: Denial-of-Service Attacks, Internet Worms, IP Traceback, BGP security. Module 3 Internet Programming: Naming and registries-DNS, LDAP, UDDI; XML Web services- Service-oriented architectures, Web servers (tomcat, IIS) and the HTTP protocol, XML, XML schema, SOAP, WSDL, XQuery, Tools and frameworks (gSOAP, Axis, .NET and mono project). Module 4 Message-level security with WS-Security, WS-Management; Grid computing-Globus, OGSA and WSRF, Cluster computing, Condor, Oscar, Peer-to-peer computing – File sharing, Remote file systems – ftp, rfs, shfs. Module 5 Basic principles of symmetric and asymmetric cryptography: Digital certificates, authentication, non-repudiation, Transport-level security with HTTPS and SSL encryption-Firewalls, Tunneling-VPN; Related Web technologies-Ajax, Voip. References:

1. Ince D, Developing Distributed & E-commerce Applications, Addision-Wesley, 2001. 2. Flanagan D, Javascript: The Definative Guide, Fourth Edition, O’Reilly, 2001. 3. Harold E.R. and Means W.S., XML in a Nutshel: A Desktop Quick Reference, First

Edition, 2001. 4. Comer D E and Droms R E, Computer Networks and Internets, Prentice Hall, 2001. 5. Maris, John and Osborne, VPN’s: A Beginners Guide, McGraw-Hill, 2001. 6. Reese G, Database Programming with JDBC and Java, Second Edition, O’Reilly,

2000.

4. ISMS2004 Pattern Recognition for Computer Security


Module 1

Supervised Pattern Recognition: Introduction to Pattern recognition systems, the design cycle, learning and adaptation, feature extraction and feature selection, Bayesian decision theory, minimum error rate classification, discriminant function and decision surfaces, the

normal density based discriminant functions; Maximum likelihood, Gaussian case, curse of dimensionality and principle component analysis. Module 2

Non parametric techniques: density estimation, parzen windows, nearest neighborhood estimation, linear discriminant functions and decision surfaces, generalized linear discriminant functions, two-category linearity separable case, Perception Algorithm. Nonmetric methods: Decision trees, Cart methods; Algorithm-independent machine learning: lack of inherent superiority of any classifier, Bias and Variance for regression and classification, resampling for estimating statistics, estimating and comparing classifiers. Module 3

Unsupervised learning and clustering: Criterion functions for clustering, Proximity Measures, hierarchical and non hierarchical (partitional) clustering, low-dimensional representations and multidimensional scaling. Module 4

Fuzzy Logic systems: Basics of fuzzy logic theory, crisp and fuzzy sets, fuzzy relations fuzzy inference, fuzzy pattern recognition and fuzzy c-Mean clustering. Module 5

Paradigms for intrusion detection systems: Misuse detection and anomaly detection, the formulation of intrusion detection task as a pattern recognition problem, data collection, and Feature extraction, various approaches-Neural networks, statistical, structural and syntactic. References

1. Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, Second Edition, John Wiley & Sons Inc., 2003.

2. Sergios Theodorides and Konstantinos Koutroumbas, Pattern Recognition, Third Edition, Academic Press, 2006.

3. Sing-Tze Bow, Pattern Recognition: Application to Large Data-Set Problems, Marcel Dekker Inc. New York and Basel, 2005.

4. Etham Alpaydin, Introduction to Machine Learning, Prentice Hall of India Private Limited, New Delhi, 2004.

5. Margret H. Dunham, Data Mining: Introductory and Advance Topics, Prentice Hall; 1 edition (September 1, 2002)

6. Earl Gose, Richard Johnsonbaugh and Steve Jost, Pattern Recognition and Image Analysis, Prentice Hall of India, 2002.

5. ISMS2005 Biometrics for Security


Module 1

Introduction: Authentication Techniques, protecting Privacy with Biometrics and Policy.

Module 2

Biometric Technologies, Finger Biometric Technologies, Face Biometric Technologies, Voice Biometric Technologies, Iris Biometric Technology.

Module 3

Implementations, Statistical measures for Biometrics.

Module 4

Security via biometrics, Spaced Domain based biometric and recognition techniques

Module 5

Correlation based biometric filters, Basic theory of Correlations filters; Design of advanced correlation filters that offer tolerance to expected impairments; Methods to implement digital correlations; Applications of correlation filters.

References

1. Paul Reid, Biometrics for Network Security, Pearson Education, 2004. 2. Saeed Khalid, Pejas Jerzy, Mosdorf Romuald, Biometrics, Computer Security

Systems and Artificial Intelligence Applications, 2006 XII, 348 p.

6. ISMS2006 Steganography, Digital Watermarking and DRM


Module 1: Steganography 1 Watermarking and Steganography, basic classification of steganography algorithms, Bitplane techniques, Transform techniques-spread spectrum, etc, Applications of steganography, Covert channels Audio data Military E-commerce, Software for steganography Module 2: Steganography 2 Steganalysis Scenarios, Steganalysis Algorithms, Software for Steganalysis

Module 3: Digital Watermarking 1

Applications and Properties of Digital Watermarking, Models of Watermarking, Using Perceptual Models Module 4: Digital Watermarking 2 Robust Watermarking, Watermark Security, Content Authentication Module 5: DRM Definition of DRM, Requirements for DRM, Components of DRM, DRM and Privacy

References 1. Cox, M. Miller, J. Bloom, J. Fridrich, T Kalker, Digital Watermarking and

Steganography, 2nd Ed. (The Morgan Kaufmann Series in Multimedia Information and Systems), 2007

2. Katzenbeisser and Pertitcolas,Information Hiding: Techniques for steganography and digital watermarking, Artech House, 2000. ISBN 1-58053-035-4.

3. Johnson, Duric, and Jajodia, Information Hiding-Steganography and Watermarking – Attacks and Countermeasures, Kluwer, 2001.

4. E. Becher, W. Buhse, D. Gunnewig, N. Rump, Digital Rights Management: Technological, Economic, Legal and Political Aspects, Springer 1 edition (January 12, 2004)

7. ISMS2007 Theory of Computation


Languages and their finite representations, regular expressions. Module 2

Deterministic and nondetermisnistic finite automata, regular expressions, Context-free grammars and languages, parse trees, ambiguity, pushdown automata. Module 3

Basic Turing machine model, Turing computability, variants of Turing machines, grammars. Module 4 Church-Turing thesis, universal Turing machines, halting problem, some undecidable problems. Module 5

Complexity classes P, NP and NP-complete, some NP-complete problems. References

1. J E Hopcroft, R Motwani and J D Ullman, Introduction to Automata Theory, Languages and Computation, Addison-Wesley, second edition, 2000.

2. L R Lewis and C H Papadimitriou, Elements of the theory of computation, Prentice-Hall, second edition, 1998.

3. Bernard Moret , The Theory of Computation, Addison Wesley; 1st edition (September 12, 1997)

4. J. Glenn Brookshear, Theory of Computation- Formal Languages, Automata and

Complexity, Addison Wesley; 1 edition (January 11, 1989)

8. ISMS2008 Statistical Methods


Module 1 Measures of central tendency, Measures of Variability, Frequency curves, Histograms, Empirical moments, Measures of Skewness and Kurtosis, Bivariate data Probability:- Axiomatic definition, Properties, Conditional probability, Bayes rule and independence of events. Module 2

Random variables, Distribution function, Probability mass and density functions, Expectation, Moments, Moment generating function, Chebyshev’s inequality. Special distributions: Bernoulli, Binomial, Poisson, Uniform, Exponential, Gamma, Normal, Joint distributions, Marginal and conditional distributions, Moments, Independence of random variables

Module 3

Inference about Population central values: Estimation of ; Choosing sample size for estimating ; statistical test for ; Choosing sample size for testing ; The level of significance of a statistical test; Inference of for normal population, Inferences comparing two population central values – Independent samples, paired data. Module 4

Inference about more than two population central values, Kruskal-Wallis test, Fisher’s Least significant difference, student – Newman-Keuls procedure Module 5 Categorical data, Inference about population proportions, Chi-square goodness of fit test Linear regression and correlation – estimating model parameters, regression parameters, estimating lack of fit in linear regression, the inverse regression problem, correlation.

Reference:

1. Lyman Ott, R. Lyman Ott, Micheal Longnecker, An introduction to statistical methods and data analysis, 6th Edn, Cengage Learning, 2008

2. R. R. Wilcox, Fundamentals of Modern Statistical Methods, Springer, New York 2001.

3. Sheldon M. Ross, Introduction To Probability And Statistics For Engineers And Scientists (Paperback) , Academic Press (2012)

4. G. W. Snedecor, and W.G.Cochran, Statistical Methods. Iowa State University Press, 1989.

5. D. J. Saville, and G. R. Wood, Statistical Methods: The Geometric Approach, Springer, New York, 1997.

6. Rudolf Jakob Freund, William J. Wilson, Statistical methods, Academic Press, 1997

9. ISMS2009 Scientific Computing


Module 1

Introduction to scientific Computing, Approximations in Scientific Computing, Computer Arithmetic, Linear Systems, Solving Linear systems, Special types of linear systems, Linear Least Squares, Problem transformations, Orthogonalization methods, Singular Value Decomposition, Comparison of methods Module 2

Eiegen Value Problems, Computing Eiegen Values and Eiegen Vectors, Generalized Eigen Value Problem Module 3

Non-linear Equations, Non-linear Equations in one dimension, Systems of Non-linear equations, Optimization problems, Unconstrained Optimizations, Non-linear least squares, Interpolation, Polynomial interpolation Module 4

Numerical Integration and differentiation, Numerical quadrature, Ordinary differential equations, Numerical Solutions to Ordinary Differential Equations, Boundary problem for ODEs, Partial differential equations Module 5

Fast Fourier Transform, Trigonometric Interpolation, FFT Algorithm, Applications of DFT, Wavelets, Random numbers and simulation, stochastic simulation, randomness and random numbers, random number generators Reference:

1. M. T. Heath, Scientific Computing, , The McGraw-Hill Companies, Inc.; 2nd edition, 2002

2. R. Hamming, Numerical Methods for Scientists and Engineers, Dover Publications; 2 edition, 1987

3. Gregoire Allaire and Alan Craig, Numerical Analysis and Optimization: An Introduction to Mathematical Modeling and Numerical Simulation (Numerical Mathematics and Scientific Computation) , Oxford University Press, USA, 2007

10. ISMS2010 High Performance Computing


Module 1

Parallel Processing and Supercomputing : Supercomputer Architecture, Vector Machines, Parallel Processors, Data Parallel Processors, Single-Instruction-Multiple-Data. Multiple-Instruction-Multiple-Data, Pipelining. Vectorization. Module 2

Parallelization of Algorithms : Parallel linear algebra routines, Loop optimizations. Implementation. Principal of Locality, Caches and Buffers. Massively Data Parallel Algorithms, Array notation, Fortran90 and HPC Fortran, Parallel and Vector C Code, Layout, Align, Replicate, Masking, Shifting, Spreading, Broadcasting, Forall Loops, ivide-and-Conquer Algorithms, Adaptive Quadrature, Correct Termination. Module 3

Algorithms and optimization : Graph algorithms, combinatorial scientific computing, Monte-Carlo simulations, linear, nonlinear and discrete optimization, Module 4

Grid Computing: Types of Computational Grids, Gid requirements of end users, application, tool and grid developers, and system managers, Cloud Computing. Module 5

Computing Platforms Operating Systems and Network Interfaces, Compilers, Languages and Libraries for the Grid, Grid Scheduling, Resource Management, Resource Brokers, Resource Reservations, Security, Accounting and Assurance

Reference:

1. J. M. Ortega, Introduction to Parallel and Vector Solution of Linear Systems, Springer; 1 edition (April 30, 1988)

2. J. J. Dongarra, I. B. Duff, D. C. Sorensen and H. A. van der Vorst, Solving Linear Systems on Vector and Shared Memory Computers, SIAM, 1991.

3. K. Hwang, Advanced Computer Architecture: Parallelism, Scalability, Programmability, McGraw-Hill, 1993.

4. Foster, I., Designing and Building Parallel Programs. Addison-Wesley, 1995. 5. Hennessy, J.L. and Patterson, D.A., Computer Architecture A Quantitative Approach.

Morgan Kaufmann, 1996.

11. ISMS2011 Digital Signal Processing


Module 1

Introduction, classification of signals, singularity functions, amplitude and phase spectra, classification of systems, simple manipulations of discrete-time signals, analog-to-digital conversion of signals. Fourier Analysis of Periodic and Aperiodic Continuous-Time Signals and Systems: trigonometric Fourier series, complex form of Fourier series Parsevals identity for Fourier series, power spectrum of a periodic function, Fourier transform, Fourier transform of some important signals, power and energy signals Module 2

Applications of Laplace Transform to System Analysis: Introduction, definition of Laplace transform, region of convergence (ROC), initial and final value theorems, convolution

integral, table of Laplace transforms, partial fraction expansions, network transfer function, s-plane poles and zeros, Laplace transform of periodic functions, and application of Laplace transformation in analyzing networks.

Module 3

z-transforms and Linear Time Invariant Systems: Introduction, definition of the z-transform, properties of the z-transform, evaluation of the inverse z-transform, properties of a DSP system, difference equation and its relationship with system function, impulse response and frequency response Discrete and Fast Fourier Transforms: Discrete convolution, discrete time Fourier transform (DTFT), fast Fourier transform (FFT), computing an inverse DFT by doing a direct DFT, composite-radix FFT, fast convolution and correlation Module 4

Finite Impulse Response (FIR) Filters: Introduction, magnitude response and phase response of digital filters, frequency response of linear phase FIR filters, design techniques for FIR filters and design of optimal linear phase FIR filters Module 5

Infinite Impulse Response (IIR) Filters: Introduction, IIR filter design by approximation of derivatives, IIR filter design by impulse invariant method, IIR filter design by bilinear transformation, butterworth filters, Chebyshev filters, inverse Chebyshev filters, elliptic filters, frequency transformation

Realization of Digital Linear Systems:

Introduction, basic realization block diagram and the signal-flow graph, basic structures for IIR systems, basic structures for FIR systems Reference:

1. S. Salivahanan, A. Vallvaraj and C. Gnanapriya, Digital Signal Processing, Tata McGraw-Hill, New Delhi, 2000

2. Sanjit K. Mitra, Digital Signal Processing, 3/e, Tata McGraw-Hill, New Delhi, 2006 3. A.V. Oppenheim and R.W. Schaffer, Digital Signal Processing, Prentice hall, NJ,

1975

12. ISMS2012 Artificial Intelligence


Scope of AI, Theorem Proving, Problem formulation and Search, Heuristic Search, Depth first, Breath first search,,best first search, mini-max search, Problem solving − Problem solving agents − Example problems − Searching for solutions

Module 2

Knowledge Representation, Syntax and semantics for first order logic, Predicate Logic, reasoning, dependency directed backtracking Handling Uncertainty, Expert Systems

Module 3

Artificial Neural Networks and Applications: Different artificial neural network models, learning in artificial neural networks, neural network applications in computational Sciences

Module 4

Fuzzy Systems and Applications: Fuzzy sets, fuzzy reasoning, fuzzy inference systems, fuzzy control, fuzzy clustering, applications of fuzzy systems, Neuro-Fuzzy Systems, genetic algorithms in search and optimization Module 5

Applications of AI and soft computing: Pattern recognitions, image processing, biological sequence alignment and drug design, robotics and sensors, information retrieval systems, share market analysis, natural language processing Reference:

1. Nilson, N.J., “ Principles of AI”, Narosa Publishing House, 1990. 2. J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: A

Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1996. 3. Rich, E., and Knight, K., “ Artificial Intelligence”, Tata McGraw Hill, 2nd Edition,

1992. 4. M. Friedman and A. Kandal, Introduction to Pattern Recognition Statistical,

Structural, Neural and Fuzzy Logic Approaches, World Scientific, 2005. 5. Timothy J. Ross, Fuzzy Logic with Engineering Applications, McGraw Hill, 1997.

13. ISMS2013 Software Engineering


Module 1

Introduction to Software Engineering and Models: History of the development of Software Engineering and its importance, Software Life cycle Models, Water fall, Incremental, Prototype, Spiral, Iterative models Module 2

Requirements Management: Requirement Analysis, SRS preparation, Requirement Review Module 3

Software Measurements, Configuration and Risk Management: Software Metrics, Software costing, Function Point analysis, COCOMO model, SCM Processes, version control, change management, Risk Management Module 4

Software Testing and Quality: Various testing Methodologies like unit testing, functional, integrated, stress testing, Performance evaluation, Defect density, Test case preparations, Quality Assurance, Quality control, Statistical Quality control, Software Defects, reviews, SQA plan, Review/inspection procedure document, checklists, Recording Defects and Actions. Module 5

Software Project Management and Process Frameworks Project Management Processes, Project Estimations, Project Planning and Tracking, scheduling, Scope Management, Communications Management, Cost Management, Integrated Change Management, Five levels of CMM, Introduction to CMM, Introduction to six sigma, DMAIC model

Reference:

1. Pressman R.S, Software Engineering: A Practitioner's Approach (6th Edition), McGraw Hill, 2005

2. Ian Sommerville, Software Engineering (7th Edition), Pearson Education Asia, 2004 3. Steve Schach, Classical and Object Oriented Software Engineering (6th Edition),

McGraw-Hill International, 2005

14. ISMS2014 Soft Computing


Module 1

Introduction: Introduction to soft computing, introduction to biological and artificial neural networks, introduction to fuzzy sets and fuzzy logic systems Module 2

Artificial Neural Networks and Applications: Different artificial neural network models, learning in artificial neural networks, neural network applications in control systems Module 3

Fuzzy Systems and Applications: Fuzzy sets, fuzzy reasoning, fuzzy inference systems, fuzzy control, fuzzy clustering, applications of fuzzy systems Module 4

Neuro-fuzzy systems: Neuro-fuzzy modeling, neuro-fuzzy control, Genetic algorithms: Simple GA, crossover and mutation, genetic algorithms in search and optimization, Introduction to Ant Colony Optimization method and Swam Intelligence Module 5 Applications of soft computing: Pattern recognitions, image processing, biological sequence alignment and drug design, robotics and sensors, information retrieval systems, share market analysis, natural language processing Reference:

1. M. Friedman and A. Kandal, Introduction to Pattern Recognition Statistical, Structural, Neural and Fuzzy Logic Approaches, (Series in Machine Perception and Artificial

Intelligence) World Scientific Pub Co Inc (December 1999). 2. Neural and Fuzzy Logic Approaches, World Scientific, 2005 3. Timothy J. Ross, Fuzzy Logic with Engineering Applications, McGraw Hill, 1997. 4. J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: A

Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1996.

5. Melanie Mitchell, An Introduction to Genetic Algorithms, Prentice Hall of India, 2004.

6. David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Professional, 1989.

15. ISMS2015 Web Technology


Module 1

HTTP and CGI, Web Server: Introduction to the web model of computing: distribution, protocols, user interface ad HTML, HTTP Protocol, Implementation of a simple HTTP Server (Hello server), CGI in Python, GET and POST methods, Apache Web Server (case study), Web.py (case study), PLT Web server Module 2 Web Programming in Python, Scheme and Java: Templating, URL mapping, CGI programming in Scheme, Introduction to Continuations, Continuation-based stateful web programming in Scheme, Web applications in Java (servlets), Web applications in JSP, Using Java Beans with JSP's Module 3 Database connectivity and Data Abstractions Database connectivity: Python, Java, Scheme, SQL Alchemy in Python, Hibernate in Java, Database abstraction layer in Scheme Module 4 Communicating Web applications and RIA Screen Scraping, API for communication: REST, web services, SOAP, DOM and XML parsing: Tidy, Xquery, RIA: CSS, Javascript, AJAX, Mashups Module 5 Performance, Scalability and Security Load testing: Profiling, Tools: siege web stress testing tool, httperf, Performance tuning and Scalability, Content Caching, Client page-load performance tuning, Replication, Load balancing, Protocols: Password Hashing, Symmetric and asymmetric keys (PKI), Security threats: (SQL injection, Invalid inputs, buffer overflows, cross-site scripting, thread safety, hidden fields), How to build secure applications References

1. Anders Miller and Michael I. Schwartzbach, An Introduction to XML and Web Technologies, Addison-Wesley, January 2006.

2. Mark Lutz, Programming Python, O'Reilly, 2006. 3. Stephanie Bodo_, Dale Green, Kim Haase, Eric Jendrock, Monica Pawlan, Beth

Stearns, The J2EE tutorial, Addison-Wesley, 2002. 4. Christian Bauer and Gavin King, Hibernate in Action, Manning Publications,

2004. 5. Ramesh Nagappan, Robert Skoczylas, Rima Patel Sriganesh, Developing Java

Web services: architecting and developing secure Web services using Java, John Wiley and Sons, 2003.

16. ISMS2016 Object Oriented Analysis and Design


Module 1 Best Practices of Software Engineering and Introduction to OMT: Develop iteratively, models and visualizations, UML, best practices in software engineering. Object modeling Technology, basic principles of object orientation, UML modeling mechanisms, Relationships, Class diagrams, Instances, Object diagrams, Packages, Interfaces.

Module 2 UML Behavioural Modeling: Use cases, Use case diagrams, Activity diagrams, Analysis- Use case behavior, finding classes and relationships Identify Design Elements, Design Mechanism Module 3

Interaction Diagrams: Sequence diagram, Collaboration Diagram, activity diagram, activity states, transitions, state chart diagram, Events Signals State machines Processes Threads State chart diagrams Module 4

Architectural Analysis and Component Diagram: 4+1 view architecture, analysis mechanism, use case realization, Components Collaborations Patterns Frameworks, Process and Threads, Concurrency, Synchronization, Collaborations, Component diagrams Module 5

Deployment Diagram: Distribution diagrams, runtime architecture, concurrency, configurations, process, nodes, networks, Deployment diagrams References

1. Grady Booch, James Rambaugh, Ivar Jacobson, The United Modeling Language User Guide- Published by Addison-Wesley, 2005

2. James Rambaugh et. al., Object Modeling and Design Prentice Hall,1991 3. Meilier Page Jones, Fundamentals of Object Oriented Design in UML, Pearson

Education, Asia, 2002

17. ISMS2017 Principles of Management


Module 1 Principles and practices of Management: Organizations and the need for Management, Role of Management theory and Management techniques, Systems approach to Management and the Management functions, Challenges of Management. Module 2

Organizational Behavior: Values, attitudes and the foundations of individual behavior, Motivation: From concepts to applications, Group Behavior and working in teams, Basic approaches to Leadership, Foundations of organization structure, Organizational culture Business Environment: Theoretical framework of Business Environment, Significance and the elements of economic, political and socio-cultural environment, Relevance of international and technological environment Module 3

Human Resource Management: Human resources planning, Group dynamics and behavior, Job satisfaction and change management, Recruitment, Selection, Training and Development, Job design, Job Appraisal, Job rotation and promotion policies, Marketing Management, Nature and functions of marketing, Services Marketing, Customer Relationship Management, Financial Management: Nature and scope of Financial Management, Overview of Financial Statements: Balance sheets, Income Statements and Statements of cash flows. Module 4

Strategic Management: Overview of Strategic Management, Strategic Management Process, Formulating the strategy: Company Mission, Internal and the External analysis of the environment, Strategic Analysis and choice: Criteria for evaluating strategic alternatives, Strategic analysis at corporate level-BCG Matrix, SWOT Analysis, Porters Model, Project

Management: Concepts of Project Management: Categories, Project Life Cycle Phases, Tools and techniques, Establishing and organizing systems and procedures for project implementation, Project direction, co-ordination and control, Project Management Performance

Module 5

Information Systems Management: Mangers view of Information Systems, Strategic uses of Information Technology, Types and levels of Information Systems, Information Systems Planning, System analysis and design, Business Process Re-engineering and Information Technology, Overview of SCM, KM, ERP, E-Governance models References

1. Harold Koontz, Heinz Weihrich, Essentials of Management, 6/e: An International Perspective, Tata McGraw-Hill, 2004

2. Philip Kotler and K.L. Kotler, Marketing Management, 12th edition, Prentice Hall, 2006.

3. James. A.OBrien, Management Information Systems (5th edition), McGraw Hill, 2006

4. John.M.Nicholas, Project Management for Business and Technology (2nd edition) Butterworth-Heinemann; 2 editions, 2004

5. Stephen P Robbins, Organizational Behavior (13th Edition), Prentice Hall, 2008

18. ISMS2018 Computational Linguistics


Module 1 Introduction to Linguistics: What is language?, Language Origin and Development, Different Theories on Language, Language families. Word, grammar and meaning, Structure of Natural Language, Design Features of Language, Morphology, Phonology, Syntax, Semantics and Lexicography Module 2 Basics of Language Technology: Introduction to Language Technology, Linguistics and Language Technology, Computational aspects of Language Technology, Computer applications of Natural Languages. Language teaching, Learning, Computer assisted Language learning and Teaching Module 3 Fundamentals of NLP: Introduction to Natural Language Processing, Regular Expressions, Language automata and Computation, Fundamentals of Corpus Analysis, Computational Morphology, POS Tagging, Issues in Indian Language POS Tagging, Computational approaches to Syntax and Grammar Module 4 Computational Models: Computational Phonology and Text to speech, Markov Models and speech Recognition, Viterbi algorithm. Word sense disambiguation and information Retrieval, Machine Translation, Natural Language Generation, Statistical NLP and N-gram Model Module 5 Applications of NLP: Linguistic Tools and Training, Morphological Analyzer, PC KIMMO, POS Tagger, Stanford Tagger, Brills Tagger, Parser (Training), Stanford Parser, Introduction

to Information Retrieval: Web Mining, Search Engines and Search Algorithms, Web Applications of Languages. Web semantics, Natural Language Tool Kit (NLTK), Wordnet References

1. Speech and Language Processing, Jurafsky, D. and J. H. Martin, Pearson Prentice

Hall., 2 edition (May 26, 2008).

2. Foundations of Statistical Natural Language Processing, Martin Manning, C. D. and H. Schütze, The MIT Press. 1999.

3. Natural Language Understanding, Allen J, The Benajmins/Cummings Publishing Company Inc. 1994.

4. Natural Language Processing A Paninian Perspective, Akshar Bharati, Chaitanya Vineet, Sangal Rajeev, Prentice Hall India. 1999.

5. Martin Rajman and Vincenzo Pallota, Speech and Language Engineering, Efpl Press, 2007

19. ISMS2019 Embedded Systems


Module 1 Introduction to Embedded Systems: Introduction, overview, characteristics of embedded computing applications, concept of real time systems, challenges in embedded systems Module 2 Embedded System Architecture: Instruction set architecture, CISC and RISC instruction set architecture, basic embedded processor, microcontroller architecture, CISC examples, 8051, RISC example, ARM, DSP processors, Harvard architecture, PIC, memory system architecture, caches Module 3 Memory Management: virtual memory, memory management, unit and address translation, I/O sub-system, busy-wait I/O, DMA, interrupt driven I/O, co-processors and hardware accelerators, processor performance enhancement, pipelining, super-scalar execution Module 4 Designing Embedded Computing Platform: Using CPU bus, bus organization, memory devices and their characteristics, RAM, ROM, UVROM, EEPROM, ash memory, DRAM, I/O devices, timers and counters, watchdog timers, interrupt controllers, A/D and D/A converters, displays, keyboards, component interfacing, memory interfacing, I/O device interfacing, designing with processors, system architecture, hardware design, FPGA based design Module 5 ARM Architecture: Registers, modes, exception handling, instruction sets, coprocessors, thumb, jazelle, ARM processor core, ARM7TDMI and ARM9TDMI pipelines, datapaths and instruction decoding, overview of ARM9E-S, ARM10, StrongARM and Xscale, ARM developer suite (ADS) overview, ARM and Thumb instruction sets Reference

1. Jonathan W. Volvano, Embedded Microcomputer Systems: Real-Time Interfacing, 2nd edition, CENGAGE-Engineering, 2006.

2. Muhammed Ali Mazidi, Janice Mazidi and Rolin McKinlay, 8051 Microcontroller and Embedded Systems, 2nd edition, Prentice Hall, 2005.

3. Kenneth J. Ayala, 8051 Microcontroller, 3rd edition, Thomson, 2005.

20. ISMS2020 Security in Distributed Environments


With more and more essential information stored on computers, security professionals need to know how to combat threats and complications. Despite recent spectacular advances in computer security regarding the explosion of services and applications, security threats are still major hurdle in the deployment of these services. This course focuses on various security aspects of distributed computing models such as Grid computing, adhoc networks, sensor networks, P2P networks and cloud computing. Module 1: Security in Grid Computing

Grid computing overview- evolution of Grid computing, benefits of Grid computing, Grid computing issues and concerns, Taxonomy of Grid security issues- architectural related issues, infrastructure related issues, Management related issues, Grid information security architecture – Grid security infrastructure, authentication in GSI, delegation in GSI, security in Globus Tool Kit, Grid authorization systems – access control models, characteristics of authorization systems, VO level and resource level authorization systems, service level security in Grid systems – DoS attacks and counter measures, QoS violation attacks and counter measures, data protection issues and sand boxing, Management of trust in the Grid – introduction, reputation and policy based trust management systems. Module 2: Security in Adhoc Networks

Wireless Ad Hoc, Sensor and Mesh Networks -Ad Hoc Networks and Applications, Sensor and Actuator Networks, Mesh Networks, Factors Influencing the Design of Wireless Ad Hoc, Sensor and Mesh Networks, Routing in Wireless Ad Hoc Networks, Routing in Wireless Sensor Networks, Security Issues in Ad Hoc Networks- Vulnerabilities, Security requirements and attacks, secure routing, key management, Attacks and defenses of routing mechanisms in adhoc and sensor networks, Privacy and Anonymity in Mobile Ad Hoc Networks, Module 3: Sensor and Satellite Networks

Authentication in wireless sensor networks, False Data Detection and Secure Data Aggregation in Wireless Sensor Networks, MAC layer attacks in sensor networks, Key management in wireless sensor networks, Underwater Sensor Networks, Satellite Networks. Module 4: Security in P2P Networks

Peer-to-Peer Computing - Potential, Benefits, and Applications, Challenges and Design Issues, Architecture of Peer-to-Peer Systems – Centralized P2P Systems, Fully Decentralized P2P Systems, Hybrid P2P Systems, Routing in Peer-to-Peer Networks – Routing in Unstructured P2P Networks, Routing in Structured P2P Networks, Routing in Hybrid P2P Networks, Security in Peer-to-Peer Networks - Routing Attacks, Storage and Retrieval Attacks, Denial-of-Service Attacks, Data Integrity and Verification, Free Riding and Fairness, Privacy and Anonymity, PKI-Based Security, Sybil attacks, Attacks in Bit torrent networks, Kazza, Limewire etc.. Module 5: Security in Cloud Computing

Cloud computing defined, framework for cloud computing, relevant technologies in cloud computing, service models, cloud deployment model, key drivers to adopting the cloud, the impact of cloud computing on users, Examples of cloud service providers, Security management in the cloud – availability management, access control, security vulnerability, patch and configuration management, key privacy concerns in the cloud, legal and regulatory implications. References

1. C. S. R. Prabhu, Grid and Cluster Computing, ISBN: 8120334280, Publisher: Prentice-hall Of India Pvt Ltd, 2008.

2. Anirban Chakrabarti, Grid Computing Security, ISBN: 978-3-540-44492-3, Springer Berlin Heidelberg, April 2007.

3. Erdal Cayirci, Chunming Rong, Security in Wireless Ad Hoc and Sensor Networks, ISBN: 978-0-470-02748-6, WILEY, March 2009.

4. Yang Xiao, Security in Sensor Networks, Auerbach Publications, August 2006. 5. Vu, Quang Hieu, Lupu, Mihai, Ooi, Beng Chin, Peer-to-Peer Computing Principles

and Applications, ISBN: 978-3-642-03513-5, Springer, 2010. 6. John F. Buford, Heather yu, Eng Keong lua, P2P Networking and Applications,

Morgan Kaufmann Publishers, November 2008. 7. Tim Mather, Subra kumaraswamy, hahed latif, Cloud Security and Privacy: An

Enterprise Perspective on Risks and Compliance, O'reilly Media, September 2009.

21. ISMS2021 Data Analytics


Module 1: Data Exploration Process flow, Exploring the problem space and solution space, mining data, types of data models, active and passive models, explanatory and predictive models, static and continuously learning models Module 2:Data Preparation Prepare, Survey and model the data, modelling with decision trees, neural network and evolution programs, missing data, stages of data preparation, data characterization, set assembly Module 3:Sampling Study Sampling, confidence, and variability. Variability of numerical and alpha variables, measuring confidence, confidence of capturing variability, problems of taking samples using variability. Module 4:Nonnumerical Variables Alphas and remapping, state space, joint distribution tables,dimensionality, practical problem simulations in R or weka or scilab.

Module 5:Normalization Techniques and Variable Processing Normalizing variable ranges, redistribution of values, retaining and replacing missing value information, series data modelling and repairing, sparse variables, issues with high dimensionality, neural net simulations in scilab or R

Reference 1. Dorian Pyle, Data Preparation for Data Mining (The Morgan Kaufmann Series in Data

Management Systems), 1999, Morgan Kaufmann; 1 edition 2. Ian H. Witten, Eibe Frank, Mark A. Hall , Data Mining: Practical Machine Learning Tools and

Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems),

Morgan Kaufmann; 3 edition (January 20, 2011)

22. ISMS2022 Digital Image Processing and Pattern Recognition


Module 1

Fundamentals of Image Processing, Elements of visual perception, Steps in Image Processing Systems, Image Acquisition, Sampling and Quantization, Pixel Relationships, Color Fundamentals and Modules, File Formats Module 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 Module 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 Module 4

Overview of Pattern Recognition, Discriminant Functions, Supervised Learning, Parametric Estimation, Maximum Likelihood Estimation, Perception Algorithm, LMSE Algorithm, Problems with Bayes Approach, Pattern Classification by Distance Functions, Minimum Distance Pattern Classifier Module 5

Unsupervised Classification, Clustering for Unsupervised Learning and Classification, Clustering Concept, C-Means Algorithm, Hierarchical Clustering Procedures, Graph theoretic Approach to Pattern Clustering, Validity of Clustering Solutions

References

1. Refael C Gonzalez and Richard E Woods, Digital Image Processing, Third Edition, Pearson Education, , 2008

2. Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, Third Edition, Brroks Col, 2008

3. Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall India, 2008

4. Madhuri A Joshi, Digital Image Processing: An Algorithmic Approach, Prentice Hall India, 2006

5. Rafael C. Gonzalez, Richard E woods, Steven L Eddins, Digital Image Processing Using MATLAB, First Edition, Pearson Education, 2004

6. Robert J. Schalkoff, Pattern Recognition: Statistical Structural and Neural approaches, John Wiley & Sons Inc., New York, 1992.

23. ISMS2023 Autonomic and Context-Aware Computing


Autonomic Computing Module 1

Overview of autonomic computing: origins, evolution, direction, Human autonomic nervous system, Creating the autonomic culture, Why is a culture important? Autonomic computing architecture, Life cycle of an autonomic element, Relationships among autonomic elements, Self-* Properties in decentralized autonomic computing. Module 2

Exploiting Emergence in autonomic systems, Dynamic collaboration in autonomic computing, Machine learning in autonomic computing systems, Algorithms and optimization methods for autonomic computing. Module 3

Autonomic networking and communications, Dynamic server allocation for autonomic service centers, Autonomic data streaming for high-performance scientific applications, Self-management of wireless networks, Standards for autonomic computing, Autonomic research challenges - Scientific challenges, Research projects in autonomic computing. Context Aware Computing Module 4

Context, context awareness and situation, Context and self-management, Structure and elements of context-aware pervasive systems – sensing, thinking, acting, an abstract architecture, infrastructures, middleware and toolkits, issues of security, privacy and efficiency. Module 5

Context-aware mobile services – context of mobile device users, location-based services, ambient services, proximity based revere auctions, context-aware artifacts, context-aware mobile software agents, context-aware addressing and communication, context-aware sensor networks, context-aware security. References

1. Manish Parashar, Salim Hariri, Autonomic Computing: Concepts, Infrastructure, and Applications, CRC Press, Taylor and Francis, 2006

2. Richard Murch, Autonomic Computing, IBM Press, Prentice Hall, 2004

3. A Practical Guide to the IBM Autonomic Computing Toolkit, IBM Press, ISBN-13: 978-0738498058, 2004

4. Nancy Forbes, Imitation of Life - How Biology is inspiring computing, MIT Press, 2004

5. Joseph L. Hellerstein, Yixin Diao, Sujay Parekh, Dawn M. Tilbury, Feedback Control of Computing Systems, John Wiley & Sons, Inc., ISBN: 9780471266372, 2004

6. Seng Loke, Context-Aware Pervasive Systems: Architectures for a New Breed of Applications, Taylor and Francis, 2007.

7. Waltnegus Dargie, Context-Aware Computing and Self-managing Systems, CRC Press, Taylor and Francis, 2009.

8. Proceedings of the 2005 through 2011 IEEE Conference on Autonomic Computing (ICAC)

9. Papers from the ACM Transactions on Autonomous and Adaptive Systems (TAAS) 10. Proceedings of the IEEE Autonomous and Autonomic Systems (ICAS) Conference

24. ISMS2024 Cryptography Standards


Module 1

Advanced Encryption Standard Hash Standards (MD5, SHA1, SHA2, HMAC)

Module 2

DL/ECKAS-DH1 and DL/ECKAS-DH2 (Discrete Logarithm/Elliptic Curve Key Agreement Scheme, Diffie-Hellman version)

DL/ECKAS-MQV (Discrete Logarithm/Elliptic Curve Key Agreement Scheme, Menezes-Qu-Vanstone version)

Module 3

IFES (Integer Factorization Encryption Scheme): Essentially RSA encryption with Optimal Asymmetric Encryption Padding (OAEP).

DL/ECIES (Discrete Logarithm/Elliptic Curve Integrated Encryption Scheme): Essentially the "DHAES" variant of ElGamal encryption

IFES-EPOC (Integer Factorization Encryption Scheme, EPOC version) Module 4

DL/ECSSA (Discrete Logarithm/Elliptic Curve Signature Scheme with Appendix): Includes four main variants: DSA, ECDSA, Nyberg-Rueppel, and Elliptic Curve Nyberg-Rueppel.

IFSSA (Integer Factorization Signature Scheme with Appendix): Includes two variants of RSA, Rabin-Williams, and ESIGN, with several message encoding methods. "RSA1 with EMSA3" is essentially PKCS#1 v1.5 RSA signature; "RSA1 with EMSA4 encoding" is essentially RSA-PSS; "RSA1 with EMSA2 encoding" is essentially ANSI X9.31 RSA signature.

DL/ECSSR (Discrete Logarithm/Elliptic Curve Signature Scheme with Recovery)

Module 5

Wired Equivalent Privacy (WEP), Wi-Fi Protected Access (WPA) X.509 Public Key Certificates ANSI X9.59 Electronic Payment Standard

References

1. FIPS PUB 197: the official AES standard, 2001 2. RFC 1321 The MD5 Message-Digest Algorithm, 1992 3. FIPS 180-3: Secure Hash Standard (SHS), 2008 4. IEEE Std 1363-2000: IEEE Standard Specifications for Public-Key Cryptography,

2000 5. IEEE Std 1363a-2004: IEEE Standard Specifications for Public-Key Cryptography -

Amendment 1: Additional Techniques, 2004 6. IEEE P1363.1/D9: Draft Standard for Public-Key Cryptographic Techniques Based

on Hard Problems over Lattices (Draft D9, January 2007) 7. IEEE P1363.2/D26: Draft Standard for Specifications for Password-based Public Key

Cryptographic Techniques (Draft D26, September 2006) 8. ITU-T Recommendation X.509 (2005): Information Technology - Open Systems

Interconnection - The Directory: Authentication Framework, 08/05. 9. IEEE Std 802.11-1997 Information Technology- telecommunications And

Information exchange Between Systems-Local And Metropolitan Area Networks-specific Requirements-part 11: Wireless Lan Medium Access Control (MAC) And Physical Layer (PHY) Specifications. 1997.

10. IEEE 802.11i standard, 2004

http://en.wikipedia.org/wiki/Wired_Equivalent_Privacy

msc cs&is syllabus

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