teaching scheme of m.tech. in computer...

TEACHING SCHEME

OF M.TECH. IN COMPUTER SCIENCE AND ENGINEERING

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

SCHOOL OF ENGINEERING, OP JINDAL UNIVERSITY

RAIGARH, CHHATTISGARH

Semester II

Sl. No

Subject Code

Subject

Periods per Week

Scheme of Examination

Total Marks

Credit L+(T+P)/2 Theory /

Practical

L T P ESE TA

1. CSE

011209 Network Security 3 1 .. 50 50 100 4

2. CSE 011210

Data Warehousing & Mining

3 1 .. 50 50 100 4

3. CSE 011211

Machine Learning 3 1 .. 50 50 100 4

4. CSE 011212 (1-9)

Elective-I

3 1 .. 50 50 100 4

5. CSE 011213

Network Security lab

.. .. 4 50 50 100 2

6. CSE 011214

Data Mining Lab .. .. 4 50 50 100 2

7. CSE 011215

Research Seminar-II

25 25 50 2

Total 12 4 8 325 325 650 22

L: Lecture, T: Tutorial, P: Practical, ESE: End Semester Examination, T.A: Teacher's Assessment.

.

Detailed Syllabus

Semester: I Branch: Computer Science & Engineering Subject: Advanced Database Engineering Code: CSE 011103 ……………………………………………………………………………………………………

Course Description: The importance of database technology has become apparent in recent years with the emergence of massive amounts of unstructured information on the web, continuously streaming data from satellites and other remote devices, and so on. Unstructured and multimedia data objects are available typically distributed over large-scale heterogeneous systems. Traditional database technology (such as the relational one), while being employed in many types of businesses to date, has been found to not meet many of the demands of newly emerging application domains. Thus new technologies in industry as well as new directions of database research have emerged. Course Objectives: This subjects aims to give students in depth information about system implementation techniques, data storage, representing data elements, database system architecture, the system catalogue, query processing and optimization, transaction processing concepts, concurrency control techniques, database recovery techniques, database security and authorization, enhanced data models for advanced applications, temporal databases, deductive databases, database technology for decision support systems, distributed databases, object oriented databases and client server architecture, advanced database concepts, and emerging technologies and applications.

Syllabus: UNIT-1:Relational mode issues

ER Model, Normalization, Query Processing, Query Optimization, Transaction Processing, Concurrency Control, Recovery, Database Tuning. UNIT- 2: Distributed Systems

Features, Distributed Database Architecture, Fragmentation, Distributed Query Processing, Distributed Transactions Processing, Concurrency Control, Recovery, Commit protocol

UNIT- 3: Object oriented Database Introduction to Object Oriented Data Bases, Approaches, Modelling and Design, Persistence, Query Languages, Transaction, Concurrency, Multi Version Locks, Recover, POSTGRES, JASMINE, GEMSTONE and ODMG Model.

Semester II

Sl. No

Subject Code

Subject

Periods per Week

Scheme of Examination

Total Marks

Credit L+(T+P)/2 Theory /

Practical

L T P ESE TA

1. CSE

011209 Network Security 3 1 .. 50 50 100 4

2. CSE 011210

Data Warehousing & Mining

3 1 .. 50 50 100 4

3. CSE 011211

Machine Learning 3 1 .. 50 50 100 4

4. CSE 011212 (1-9)

Elective-I

3 1 .. 50 50 100 4

5. CSE 011213

Network Security lab

.. .. 4 50 50 100 2

6. CSE 011214

Data Mining Lab .. .. 4 50 50 100 2

7. CSE 011215

Research Seminar-II

25 25 50 2

Total 12 4 8 325 325 650 22

L: Lecture, T: Tutorial, P: Practical, ESE: End Semester Examination, T.A: Teacher's Assessment.

Semester: II Branch: Computer Science & Engineering Subject: Network Security Code: CSE 011209 …………………………………………………………………………………………………..

Course Description:

The course covers the Security Principles and practices include Information System Security Principles, Information System Security Management, Operating System and Applications Security. The Topics include Network Security Fundamentals, Security protocols, security devises, Cryptographic algorithms and protocols, Communication security and threats and its assessment, testing and evaluations.

Course Objectives: Learning objectives include:

1. To identify and evaluate threats to network security and data loss.

2. To install, configure, and evaluate firewalls.

3. To Configure and appraise Intrusion Detection and Response Systems.

4. To install, configure, and evaluate VPN (Virtual Private Network) Technology.

5. To evaluate the security of a wireless network system.

6. To create and evaluate packet captures.

Syllabus: Unit: 1: Introduction to Information System Security Management, Key security principles, Risk Management.

Unit-2: Operating System and Applications Security: Web browser security, E-mail Security, Web security, DNS Security, Linux Security, Windows security.

Unit-3: Network Security: Network Security Protocol, Wireless security, Network Security architecture.

Unit-4: Secure Communication: Cryptography protocols and Algorithms, Steganography, digital watermarking.

Unit-5: Security threats and Response: Intrusion detection system, Intrusion prevention

system, Firewall, honey pots and incidence response.

Text books:

1. Eric Cole, “Network Security Bible”, John Wiley & Sons, 31-Mar-2011.

2. William Stallings, “Cryptography and Network Security”: Principles and Standards”, Prentice Hall India, 5th Edition, 2007.

3. Charlie Kaufman, Radia Perlman and Mike Speciner, “Network Security: Private Communication in a public world”, Prentice Hall India, 2nd Edition, 2011.

Course Outcomes: Upon successful completion of this course, the student will be able to:

1. Evaluate external and internal threats to an organization.

2. Apply elementary security measures in day to day life.

3. Design and modify network and wireless security protocols using different tools.

4. Assess security threats for an application/organization and propose appropriate preventive measures.

Semester: II Branch: Computer Science & Engineering Subject: Data Warehousing & Mining Code: CSE 011210 …………………………………………………………………………………………………..

Course Description:

This course is designed to introduce the core concepts of data mining, its techniques, implementation and benefits. It will also identify industry branches which most benefit from DM (such as retail, target marketing, fraud protection,

health care and science, web and ecommerce). The course also focus on business solutions and results by presenting detailed case studies from the real world and finish with implementing leading mining tools on real (public domain) data.


1. Learning how to gather and analyze large sets of data to gain useful business understanding.

2. Learning how to produce a quantitative analysis report/memo with the necessary information to make decisions.

3. Describing and demonstrating basic data mining algorithms, methods, and tools.

4. Identifying business applications of data mining.

5. Overview of the developing areas - web mining, text mining, and ethical

aspects of data mining.

Syllabus:

UNIT – 1: Introduction

Data Warehousing – Introduction, Overview and Concepts: Need for data warehousing, Basic elements of data warehousing, Architecture And Infrastructure, Data Design And Data Representation, OLAP in data warehouse – ROLAP, MOLAP, HOLAP, Various Data Warehouse Schemas.

UNIT – 2: Linear Data Structure Data Mining Primitives, Languages, and System Architectures, Query language, Designing GUI based on a data mining query language, Knowledge Discovery in Databases (KDD), Principal Component Analysis.

UNIT – 3: Non-linear Data Structure

Data Mining–Characterization, Discrimination & DMQL, analysis of attribute relevance, Mining class comparisons, Mining descriptive statistical measures in large databases, DMQL.

UNIT – 4: Analysis & Design Methodology of Algorithms

Data Mining Algorithms: Association rules, Classification and Prediction. Apriori Algorithm, Tree Based Algorithms, Classification algorithms, Prediction Techniques, Regression Models.

UNIT- 5: Advance Algorithms Clustering algorithms, Web Mining, Spatial Mining, Data Mining of Image and Video, A case study.

Text Books:

1. J. Han & M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2nd Ed, 2006.

2. M. J. A. Berry and G. Linoff, Mastering Data Mining: The Art and Science of Customer Relationship Management, Wiley Computer Publishing, 2000.

3. Prabhu, Data ware housing concepts, Techniques, Products and Applications, Prentice hall of India

Reference Books:

1. P. Adriaans & D. Zantinge, Data Mining, Addison Wesley, 1996.

2. R. Mattison, Data Warehousing: Strategies, Tools and Techniques, McGraw Hill, 1996.

3. P. Ponniah, Data Warehousing Fundamentals: A Comprehensive Guide for IT Professionals, Wiley, 2001.

4. Soman K P, “Insight into Data Mining: Theory & Practice” , Prentice hall of India

5. M.H. Dunham, “Data Mining Introductory and Advanced Topics”, Pearson Education.

6. Ralph Kimball, “The Data Warehouse Lifecycle toolkit”, John Wiley.

Course Outcome: Upon successful completion of this course, the student will be able to:

1. Gather requirements for data warehousing.

2. Explain data warehouse architecture.

3. Design a physical and dimensional model for data warehousing.

4. Apply Data Mining Query language for various purposes.

5. Cluster/classify the large data set through various techniques.

Semester: II Branch: Computer Science & Engineering Subject: Machine Learning Code: CSE 011211 …………………………………………………………………………………………………..

Course Description: This course is to review modern topics in machine learning with a strong emphasis in concepts. The course will deepen the student's knowledge of how to

build computer systems that learn from experience. It assumes basic knowledge of introductory machine learning and a good background on probability and statistics, linear algebra, and optimization methods. We will cover modern topics such as learning theory, meta-learning, domain adaptation, self-adaptive learning algorithms, deep learning, Gaussian processes, and kernel methods.

Course Objectives:

Learning objectives include:

1. Be able to formulate machine learning problems corresponding to different applications.

2. Understand a range of machine learning algorithms along with their strengths and weaknesses.

3. Understand the basic theory underlying machine learning.

4. Be able to apply machine learning algorithms to solve problems of moderate complexity.

5. Be able to read current research papers and understand the issues raised by current research.

Syllabus:

UNIT- 1: Introduction Learning Problems, Perspectives and Issues, Concept Learning, Version Spaces and Candidate Eliminations, Inductive bias, Decision Tree learning, Representation Algorithm, Heuristic Space Search.

UNIT- 2: 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 Evaluation and Learning.

UNIT- 3: Bayesian and Computational Learning Bayes Theorem, Concept Learning, Maximum Likelihood, Minimum Description Length Principle Bayes Optimal Classifier, Gibbs Algorithm, Naïve Bayes Classifier Bayesian Belief Network, EM Algorithm, Probability Learning, Sample Complexity, Finite and Infinite Hypothesis Spaces, Mistake Bound Model.

UNIT- 4: Instant Based Learning K-Nearest Neighbor Learning, Locally weighted Regression, Radial Bases Functions and Case Based Learning. UNIT-5: Advanced Learning Learning Sets of Rules, Sequential Covering Algorithm, Learning Rule Set, First Order Rules, Sets of First Order Rules, Induction on Inverted Deduction Inverting Resolution, Analytical Learning, Perfect Domain Theories Explanation Base Learning, FOCL Algorithm, Reinforcement Learning, Temporal Difference Learning.

Text Books:

1. Tom M. Mitchell, “Machine Learning”, McGraw,Hill edition, 1997.

2. Ethem Alpaydin, “Introduction to Machine Learning (Adaptive

Computation and Machine Learning)”, The MIT Press 2004

Reference Books:

1. T. Hastie, R. Tibshirani, J. H. Friedman, “The Elements of Statistical Learning”, Springer Verlag, 2001.

2. Pattern recognition and machine learning by Christopher Bishop,

Springer Verlag, 2006.

Course Outcome: Upon successful completion of this course, a student will be able to:

1. Classify and examine well, known real world datasets using supervised, unsupervised and semi, supervised learning algorithms.

2. Design and Implement various machine learning algorithms.

3. Compare different algorithm based on various performance parameters.

ELECTIVE - I

S. No Subject Code Subject Name

1 CSE 011212 (1) Software Engineering

2 CSE 011212 (2) IoT Security

3 CSE 011212 (3) Design and Development of IoT Application

4 CSE 011212 (4) RTOS and Embedded OS

5 CSE 011212 (5) Quantum Computing

6 CSE 011212 (6) Wireless Sensor Network

7 CSE 011212 (7) Cloud Security

8 CSE 011212 (8) Enterprise storage system

9 CSE 011212 (9) Performance Evaluation and Reliability of

Information Systems

Semester: II Branch: Computer Science & Engineering Subject: Software Engineering Code: CSE 011212 (1) …………………………………………………………………………………………………..

Course Description: This course introduces the concepts and methods required for the construction of software interactive system. It will also include quality measures and maintenance of software. It also focus on the Reliability Techniques for software development.

Course Objectives:

Learning objectives include:

1. To introduce software project and to understand about the different software processes & their uses.

2. To understand the software life cycle models.

3. To manage the large projects.

4. To understand Reliability Techniques for software development.

Syllabus: Unit–1:

Introduction, software life-cycle models, CMMI

Unit-2: SRS, SDS, formal requirements specification and verification - axiomatic and algebraic specifications Requirement Engineering Processes.

Unit-3:

Software Design: UML; computer-aided software engineering (CASE), Verification and Validation: Software Testing, Quality assurance, Maintenance.

Unit–4: Project Management: activities, planning, scheduling, Risk Management.

UNIT–5:

Reliability Techniques, Models of concurrency, Static analysis, Security vulnerabilities/attacks, Vulnerability detection.

Text Books:

1. Sommerville: Software Engineering, Pearson Education Publication, 7th

edition.

2. M. Ben-Ari, "Principles of concurrent and distributed programming", Addison-Wesley, 2006 "Handbook of model checking", Springer, 2014.

Reference Books:

1. R. S. Pressman: Software Engineering: A Practiioners Approach, 5th Edn., TMA, New Delhi.

2. M. Ben-Ari, "Principles of concurrent and distributed programming",

Addison-Wesley, 2006 "Handbook of model checking", Springer, 2014.

3. Brian Chess and Jacob West, "Secure programming with static analysis", Addison Wesley, 2007 Additional research papers.

4. J. F. Peters & W. Pedrycz– Software Engineering, John Wiley & Sons,Inc.

2000.

5. A.Behforooz & F.J. Hudson– Software Engineering Fundamentals, Oxford Univ. Press, New York, 2000.

6. Rajib Mall, Fundamentals of Software Engineering, Prentice Hall India.

7. Pankaj Jalote, An integrated approach to Software Engineering,

Springer/Narosa.

8. Ian Sommerville, Software Engineering, Addison-Wesley.


1. Understand the issues affecting the organization, planning and control of

software based system development.

2. Analyze and design of a small software intensive system.

3. Manage risk and measure the software quality and reliability.

Semester: II Branch: Computer Science & Engineering Subject: IoT Security Code: CSE 011212 (2) ……………………………………………………………………………………………………….

Course Description: The main objective of this course is to make students aware about Threats & Security issues in IoT system design. This course is about the most recent trends and activities in the internet capabilities and concerns about programmed devices. It also conveys complexities and areas of necessary awareness about IoT applications.

Course objectives: The student will be:

1. Understand the types of attacks and threats, privacy preservation in IOT, Trust model in IoT infrastructure.

2. Understand the types of services offered through cloud computing, virtualization & IT infrastructure security capabilities.

3. The current state of data security and the storage of data in the cloud, privacy aspects to consider within the context of cloud computing.

4. Identify the security aspects in IoT data computations and social context based privacy in IoT platforms.

Syllabus: Unit –1: IOT Threats, Attacks, Authentication & Privacy Preservation

Internet of Things (IoT), Threats, Attack, Defense, and Network Robustness of IoT, Privacy Preservation Data Dissemination, Privacy Preservation for IoT Used in Smart Buildings, Trust and Authentication.

Unit-2: Cloud Security: Introduction to cloud computing, Cloud architectures Overview of cloud security, Data security, Secure Computation: The Network, Host and Application Level, Data Security Mitigation , Provider Data and Its Security.

Unit –3: Security Management & Privacy in Cloud

Security Management Standards, Security Management in the Cloud, Availability Management in SaaS, PaaS, IaaS, Access Control, Security Vulnerability, and Configuration Management, PRIVACY In cloud.

Unit- 4: IOT Data Security And Social Awareness Computational Security for the IoT, Security Protocols for IoT Access Networks, Security and impact of IoT on mobile networks.

Unit-5: Implementation & Studies of IoT based Applications Security and Privacy for IoT, Case Studies: Smart Home, Smart Grid Network, Modern Vehicle, Wearable Computing & BYOD, Mobile HealthCare, Smart City.

Text Books:

1. Cloud Security and Privacy An Enterprise perspective on Risk and Compliance, Tim Mather, Subra Kumaraswamy, and Shahed Latif, O’Reilly.

2. Security and Privacy in Internet of Things Models Algorithms and Implementations, Fe Hu, CRC Press.

Reference Books:

Cloud Security by Ronald Krutz and Russell Dean Vines, Wiley-India


1. Identify the attacks, threats and privacy preservation in IoT and trust model in IoT infra structure.

2. Assess privacy aspect, data storage and security management frameworks and Standards.

3. Define and Understand IT infrastructure security capabilities and services offered through cloud computing.

4. Demonstrate social context based privacy in IoT and data computations security issues in IoT.

Semester: II Branch: Computer Science & Engineering Subject: Design and Development of IoT Application. Code: CSE 011212(3) ……………………………………………………………………………………………………….

Course Description: The course provides insight into the Internet of Things information organization, modelling entities and relationships, methods of data storage, and methods for detection and search, in particular shared user and sensor information. The knowledge is then used to provide detailed insights into how this can be used to create IoT applications.

Course Objectives:

1. Learning key IoT concepts including identification, sensors, localization, wireless protocols, data storage and security.

2. Explore IoT technologies, architectures, standards, and regulation.

3. Realize the value created by collecting, communicating, coordinating, and leveraging the data from connected devices.

4. Examine technological developments that will likely shape the industrial landscape in the future.

5. Understand how to develop and implement your own IoT technologies, solutions, and applications.

Syllabus:

Module1: IOT Framework Architecture of IOT, Opportunities for IoT, RFID Technology, and Importance of

security, privacy, and authenticity, Linked data, Enterprise data – shared vs. public vs. private, features of a distributed architecture for a system of things.

Module 2: Technologies Communication architecture- Network protocol stack, RF: ZigBee, Blue Tooth, BLE, Zwave, Mesh network. Communication Channels: GSM/GPRS, WiFi. IoT protocols: MQTT/MQTTS, CoAP, 6LoWPAN, like TCP, UDP, HTTP/S. Comparison of the different IoT protocols. Wireless protocols, connectivity options, Bluetooth Smart Connectivity, Data storage and analysis, Cloud and Application layer design, Data consistency, Localization: Localization algorithms, Indoor localization, Localization for mobile systems.

Module 3: Applications Sensors technology, emerging descriptive data standards for IoT and sensors, IoT framework for smart buildings, smart homes, and smart health application development. Smart tools for IoT, Arduino based IoT application development

Text Books:

1. Vijay Madisetti and Arshdeep Bahga, “Internet of Things (A Hands-on-

Approach)”, 1st Edition, VPT, 2014.

2. Francis daCosta, “Rethinking the Internet of Things: A Scalable Approach to Connecting Everything”, 1st Edition, Apress Publications, 2013.

3. Jan Holler, Vlasios Tsiatsis, Catherine Mulligan, Stefan Avesand, Stamatis Karnouskos, David Boyle, “From Machine,to,Machine to the Internet of Things: Introduction to a New Age of Intelligence”, 1st Edition, Academic Press, 2014.

Reference Publications:

1. Internet of Things -Global Technological and Societal Trends -Smart Environments and Spaces to Green ICT; Ed. Ovidiu Vermesan,SINTEF, NO & Peter Friess, EU, BE; The River Publishers Series in Communications; ISBN: 978-87-92329-67-7.

2. Internet of Things -Legal Perspectives; Rolf H. Weber and Romana Weber;

Förlag: Springer, 2010. ISBN10: 3642117090; ISBN13: 9783642117091.

3. Proceedings of the Second International Workshop on the Web of Things (WoT 2011) in conjunction with the Ninth International Conference on Pervasive Computing (Pervasive 2 011) San Francisco, June 12-15 2011.

4. “The Internet of Things”, Proceedings of the 20th Tyrrhenian Workshop on Digital Communications. Ed.Daniel Giusto, Antonio Iera, Giacomo Morabito and Luigi Atzori; Springer, 2010.

Course Outcome:

Upon successful completion of this course, the student will be able to:

1. Understand the vision of IoT from a global context.

2. Determine the Market perspective of IoT.

3. Use of Devices, Gateways and Data Management in IoT.

4. Building state of the art architecture in IoT.

5. Application of IoT in Industrial and Commercial Building Automation and Real World Design Constraints.

Semester: II Branch: Computer Science & Engineering Subject: RTOS and Embedded OS Code: CSE011212(4) ………………………………………………………………………………………………………

Course Description:

This course is related to real time systems and embedded computing systems. This course introduces various applications of real time systems, presentation of timing constraints involved and their mathematical model. This course also presents the computations related to resource sharing, routing and database management. It also presents the real time communication and related algorithms.


1. To study Real Time Applications, Commonly Used Approaches and OS services, types and factors RTOS.

2. To study Concept and of Task scheduling, Interrupt Handling.

3. To study Comparison and application of various RTOS.

4. To study Computing and Communications required for the real time embedded systems.

Syllabus:

Unit -1: Real Time Applications, Types Of Real Time Systems, Reference Model Of Real Time Systems

Unit-2: Commonly Used Approaches to Real Time Scheduling, Periodic, Aperiodic And Sporadic Jobs Scheduling In Priority Driven Systems, Resources And Resource Access Control, Other Operating Systems Issues Unit-3: Concept and Fundamentals of RTOS, Real Time Applications, Types Of Real Time Systems, Reference Model Of Real Time Systems, essential features, RTOS Kernel Function Unit- 4: OS services. Operating Modes. Threads, Context Switching overheads, Scalability, Embedding with application code. Task Scheduling, Interrupt handling, Inter task communication.

Unit- 5: Comparison and application of various RTOS. RTOS examples Vx Works. Advance topics on Embedded Issues.

Text Books:

1. J. W. S. Liu, Real time Systems, Pearson Education, 6th impression,

2008.

2. R. Mall, Real Time Systems, Pearson, 2007.

Reference Books:

1. C. M. Krishna and K. G. Shin, Real Time Systems, McGraw Hill, reprinted 2004.

2. P. A. Laplante, Real Time Systems Design & Analysis, Willey, 3rd Ed, 2004.

Course Outcome:

Upon successful completion of this course, the student will be able:

1. To present the mathematical model of the system.

2. To develop real time algorithm for task scheduling.

3. To understand the working of real time operating systems and real time database.

4. To work on design and development of protocols related to real time communication.

Semester: II Branch: Computer Science & Engineering Subject: Quantum Computing Code: CSE011212 (5) ………………………………………………………………………………………………………

Course Description: This course provides an introduction to the theory and practice of quantum computation. Topics covered include: physics of information processing, quantum logic, quantum algorithms including Shor's factoring algorithm and Grover's search algorithm, quantum error correction, quantum communication, and cryptography.


1. To read and understand a computer science view towards quantum

computing with a stress on quantum algorithms.

Syllabus: Unit–1: Fundamental Concepts Global Perspectives, Quantum Bits, Quantum Computation, Quantum Algorithms, Quantum Information, Postulates of Quantum Mechanisms. Unit–2: Quantum Computation Quantum Circuits – Quantum algorithms, Single Orbit operations, Control Operations, Measurement, Universal Quantum Gates, Simulation of Quantum Systems, Quantum Fourier transform, Phase estimation, Applications, Quantum

search algorithms – Quantum counting – Speeding up the solution of NP – complete problems – Quantum Search for an unstructured database. Unit-3: Quantum Computers Guiding Principles, Conditions for Quantum Computation, Harmonic Oscillator Quantum Computer, Optical Photon Quantum Computer – Optical cavity Quantum electrodynamics, Ion traps, Nuclear Magnetic resonance. Unit- 4: Quantum Information’s Quantum noise and Quantum Operations – Classical Noise and Markov Processes, Quantum Operations, Examples of Quantum noise and Quantum Operations – Applications of Quantum operations, Limitations of the Quantum operations formalism, Distance Measures for Quantum information.

Unit-5: Quantum Error Correction: Introduction, Shor code, Theory of Quantum Error –Correction, Constructing Quantum Codes, Stabilizer codes, Fault – Tolerant Quantum Computation, Entropy and information – Shannon Entropy, Basic properties of Entropy, Von Neumann, Strong Sub Additivity, Data Compression, Entanglement as a physical resource.

Text Books:

1. Micheal A. Nielsen. & Issac L. Chiang, “Quantum Computation and Quantum Information”, Cambridge University Press, Fint South Asian edition, 2002.

Reference Books:

1. John Preskill’s lecture notes

http://www.theory.caltech.edu/people/preskill/ ph229/

2. David Mermin’s lecture notes http://people.ccmr.cornell.edu/∼mermin/qcomp/ CS483.html

Course Outcome:

Upon successful completion of this course, the student will be able to:

1. Be able to analyze simple quantum algorithms and possibly see abstractions of the kind in various contexts

2. Appreciate the speed up from quantum parallelism

3. Have enough familiarity to read and understand the research papers in Quantum Algorithms

Semester: II Branch: Computer Science & Engineering Subject: Wireless Sensor Network Code: CSE011212 (6) ………………………………………………………………………………………………………

Course Description:

This course covers fundamentals of wireless network technology and distributed sensor networks. It also covers various WSN applications in areas of environmental monitoring, smart energy systems, battle field surveillance, home automation, medical monitoring, mobile computing, etc. Course touches upon integrated network engineering, embedded system engineering and sensor

technology in the context of WSN.


1. To understand the basic WSN technology and supporting protocols, with emphasis placed on standardization basic sensor systems and provide a survey of sensor technology.

2. Understand the Sensor management, sensor network middleware, operating systems.

3. To learn designing and simulating simple WSN protocol using simulators.

Syllabus: Unit –1: Overview of Wireless Sensor Networks Challenges for Wireless Sensor Networks, Enabling Technologies for Wireless Sensor Networks. Unit – 2: Architectures Single Node Architecture, Hardware Components, Energy Consumption of Sensor Nodes, Operating Systems and Execution Environments, Network Architecture, Sensor Network Scenarios, Optimization Goals and Figures of Merit, Gateway Concepts. Unit-3: Networking Sensors Physical Layer and Transceiver Design Considerations, MAC Protocols for Wireless Sensor Networks, Low Duty Cycle Protocols And Wakeup Concepts, S,MAC, The Mediation Device Protocol, Wakeup Radio Concepts, Address and Name Management, Assignment of MAC Addresses, Routing Protocols, Energy, Efficient Routing, Geographic Routing.

Unit- 4: Infrastructure Establishment Topology Control, Clustering, Time Synchronization, Localization and Positioning, Sensor Tasking and Control.

Unit-5: Sensor Network Platforms And Tools Sensor Node Hardware, Berkeley Motes, Programming Challenges, Node level software platforms, Node level Simulators, State centric programming.

Text Books:

1. Holger Karl & Andreas Willig, "Protocols And Architectures for Wireless

Sensor Networks" , John Wiley, 2005.

2. Feng Zhao & Leonidas J. Guibas, “Wireless Sensor Networks, An Information Processing Approach", Elsevier, 2007.

Reference Books:

1. Kazem Sohraby, Daniel Minoli, & Taieb Znati, “Wireless Sensor

Networks,Technology, Protocols, And Applications”, John Wiley, 2007.

2. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.

Course Outcomes: Upon successful completion of this course, the student will be able to:

1. Implement the WSN routing protocols.

2. Identify medium access control protocols and address physical layer

issues.

3. Implement the transport layer protocols for sensor networks.

4. Identify the WSN design requirements.

5. Identify the WSN software level platform.

Semester: II Branch: Computer Science & Engineering Subject: Cloud Security Code: CSE011212 (7) ………………………………………………………………………………………………………

Course Description:

This course covers the principles and best practices of cloud security. It includes broad set of technologies deployed to protect data, applications, and the associated infrastructure. It also covers legal and compliances issues related to cloud.


1. To compare modern security concepts as they are applied to cloud computing.

2. To assess the security of virtual systems.

3. To understand the types of services offered through cloud computing

virtualization & IT infrastructure security capabilities.

4. To familiarize with compliance issues that arises from cloud security.

Syllabus: Unit –1: Security Concepts Confidentiality, privacy, integrity, authentication, non-repudiation, availability, access control, defense in depth, least privilege, how these concepts apply in the cloud, PaaS, IaaS and SaaS. e.g. User authentication in the cloud; Cryptographic

Systems- Symmetric cryptography, public-key cryptography, hashing, digital signatures, public-key infrastructures, key management, OpenSSL. Unit – 2: Multi-Tenancy Issues Isolation of users/VMs from each other. How the cloud provider can provide this; Virtualization System Security Issues- e.g. ESX and ESXi Security, ESX file system security, storage considerations, backup and recovery; Virtualization System Vulnerabilities- Management console vulnerabilities, management server vulnerabilities, administrative VM vulnerabilities, guest VM vulnerabilities, hypervisor vulnerabilities, hypervisor escape vulnerabilities, configuration issues, malware (botnets etc).

Unit-3: Virtualization System-Specific Attacks Guest hopping, attacks on the VM (delete the VM, attack on the control of the VM, code or file injection into the virtualized file structure), VM migration attack, hyperjacking.

Unit- 4: Technologies for Virtualization-Based Security Enhancement IBM security virtual server protection, virtualization-based sandboxing; Storage Security- HIDPS, log management, Data Loss Prevention. Location of the Perimeter. Unit-5: Legal And Compliance Issues:

Responsibility, ownership of data, right to penetration test, local law where data is held, examination of modern Security Standards (eg PCIDSS), virtualization, compliance for the cloud provider vs. compliance for the customer.

Text Books:

1. Tim Mather, Subra Kumara swamy, Shahed Latif, “Cloud Security and Privacy: An Enterprise Perspective on Risks and Compliance” O'Reilly Media; 1 edition [ISBN: 0596802765], 2009.

2. Ronald L. Krutz, Russell Dean Vines, “Cloud Security” [ISBN: 0470589876], 2010.

3. John Rittinghouse, James Ransome, “Cloud Computing” CRC Press; 1 edition [ISBN: 1439806802], 2009.

Reference Books:

1. J.R. ("Vic") Winkler, “Securing the Cloud” Syngress [ISBN: 1597495921] 2011.

2. Cloud Security Alliance, “Security Guidance for Critical Areas of Focus in Cloud Computing” 2009.

3. Vmware “VMware Security Hardening Guide” White Paper, June 2011.

4. Cloud Security Alliance 2010, “Top Threats to Cloud Computing” Microsoft 2013.

5. Timothy Grance; Wayne Jansen;NIST “Guidelines on Security and Privacy in Public Cloud Computing”, 2011.


1. Asses IT infrastructure security capabilities and services offered through cloud computing.

2. Design and Implement data storage and security management frameworks and standards.

3. Identify attacks, threats and privacy preservation trust model in cloud infra structure.

4. Identify various compliance and legal issues with reference to cloud security.

Semester: II Branch: Computer Science & Engineering Subject: Enterprise Storage System Code: CSE011212 (8) ………………………………………………………………………………………………………

Course Description: This course provides a comprehensive introduction to storage technology in an increasingly complex IT environment. It builds a strong understanding of underlying storage technologies and prepares you to learn advanced concepts and technologies. The module focuses on architectures, features and benefits of Intelligent Storage Systems; networked storage technologies such as FC,SAN, NAS and IP,SAN; long term archiving solutions, the increasingly critical area of information security and the emerging field of storage virtualization technologies.


1. Storage system architecture

2. Overview of virtualization technologies

3. Storage area network (SAN) and network attached storage (NAS)

4. Business continuity and storage security

5. Storage infrastructure management processes

Syllabus: Unit –1: Storage Systems Data Classification, Storage Evolution and Data Center infrastructure. Host components, Connectivity, Storage, and Protocols. Components of a disk drive, physical disk and factors affecting disk drive performance. RAID level

performance and availability considerations. Components and benefits of an intelligent storage system. Unit – 2: Storage Networking Technologies Direct Attached Storage (DAS) architecture, Storage Area Network (SAN) attributes, components, topologies, connectivity options and zoning. FC protocol stack, addressing, flow control, and classes of service. Networked Attached Storage (NAS) components, protocols, IP Storage Area Network (IP SAN) iSCSI, FCIP and FCoE architecture. Content Addressed Storage (CAS) elements, storage, and retrieval processes.

Unit-3: Virtualization: Block,level and file,level storage virtualization technology, virtual provisioning and cloud computing.

Unit- 4: Business Continuity Business Continuity measurement, terminologies, and planning. Backup designs, architecture, topologies, and technologies in SAN and NAS environments. Local and Remote replication using host and array,based replication technologies such as Synchronous and Asynchronous methods. Unit-V: Storage Security and Management:

Storage security framework and various security domains. Security implementation in SAN,NAS and IP,SAN networking. Monitoring and Storage management activities and challenges

Text Books:

1. EMC, Information Storage and Management [ISBN: 978,0470294215]

2. Richard Barker, Paul Massiglia 2002, Storage area network essentials, Wiley New York [ISBN: 978,0471034452]

3. Ulf Troppens, Rainer Erkens, Wolfgang Mueller,Friedt, Rainer Wolafka, Nils Haustein, Storage Networks Explained [ISBN: 978,0470741436]

Reference:

1. W. Curtis Preston 2002, Using SANs and NAS, O'Reilly & Associates Sebastopol, Calif. [ISBN: 978,0596001537]

2. Himanshu Dwivedi 2006, Securing storage, Addison,Wesley Upper Saddle River, NJ [ISBN: 978,0321349958]

Course outcome: Upon successful completion of this course, the student will be able to:

1. Evaluate various storage classifications and technologies.

2. Analyze storage architectures, processes, components and how they relate to virtualization.

3. Justify the implementation of a range of storage solutions to enable business continuity.

4. Analyze storage security design, implementation, monitoring and management.

Semester: II Branch: Computer Science & Engineering Subject: Performance Evaluation and Reliability of Information System Code: CSE011212 (9) ………………………………………………………………………………………………………

Course Description:

This course provides emphasizes on evaluating performance of Information system based on different criterion. It also covers approaches to model an information system as well as methods to measure the reliability of system.

Course objective:

1. To understand various approaches to model the information.

2. To understand the performance evaluation measures of information system.

3. To understand the reliability measures of Information system.

Syllabus: Unit-1: Review of probability and statistics, Stochastic processes, Markov Models. Unit-2: Parameter Estimation and hypothesis testing. Models of information systems, introduction to reliability measures. Unit-3: Estimation of MTF and other reliability parameters. Software metrics and software reliability models. Unit-4: Queuing network models, Workload design, Benchmarks. Unit-5: Estimations of performance metrics, case studies.

Course Outcome: At the end of the course students would be able to:

1. Represent the information through various suitable models.

2. Develop performance evaluation metrics for information systems.

3. Measure the quality of Information system as a whole.

Text Books and Reference Books:

1. Handbook of Software Reliability Engineering by Michael R. Lyu.

2. Software Metrics: A Rigorous and Practical Approach by Norman Fenton and James Bieman.

3. Markov Processes for Stochastic Modeling by Oliver Ibe.

https://www.amazon.com/s/ref=dp_byline_sr_book_1?ie=UTF8&text=Michael+R.+Lyu&search-alias=books&field-author=Michael+R.+Lyu&sort=relevancerank

https://www.amazon.com/s/ref=dp_byline_sr_book_1?ie=UTF8&text=Oliver+Ibe&search-alias=books&field-author=Oliver+Ibe&sort=relevancerank

Semester: II Branch: Computer Science & Engineering Subject: Network Security Lab Code: CSE011213 ……………………………………………………………………………………………………..

Course Detail: Lab includes implementation of various Private and public key cryptography algorithms like DES, AES, IDEA, RSA etc. It covers the concept of Hashing and

Digital signature algorithm. Course also stresses on using various APIs for different application.

Semester: II Branch: Computer Science & Engineering Subject: Data Mining Lab Code: CSE011214 ……………………………………………………………………………………………………..

Course Detail:

This course aims at learning and using various tools for data mining, OLAP

operations, data pre processing techniques, frequent pattern mining algorithms, different type of classification algorithms and different type of clustering algorithms.

teaching scheme of m.tech. in computer...

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