1. COURSE SUMMARY

This course will focus on issues related to the design and implementation of secure data stores. Emphasis will be placed on multilevel security in database systems, covert channels, and security measures for relational and object-oriented database systems.

2. LEARNING OUTCOMES
1 Knowledge and Understanding

1. Understand the basic concept of database security

2. Apply appropriate access controls and authentication techniques at different levels

3. Understand, identify and find solutions to security problems in statistical database systems

4. Understand the model for protecting new generation database systems

2 Cognitive/Intellectual skills/Application of Knowledge

1. Integration of theory and practice within the constraints of a given framework
2. Analyse failures in computer systems and devise ways to prevent them.
3. Select and apply appropriate mathematical methods for modelling and analysing computer engineering and information security problems.
4. Use scientific and engineering principles in the development of solutions to problems in computer engineering and information security.

3 Communication/ICT/Numeracy/Analytic Techniques/Practical Skills

1. Prepare technical reports and deliver technical presentations.
2. Plan the installation and maintenance of computer hardware, software, computer systems and equipment.
3. Use computational tools and packages appropriate to computer engineering and information security

4 General transferable skills

1. Have the capacity for self-learning in familiar and unfamiliar situations.
2. Use competently information technology (ICT).
3. Communicate effectively (written, verbal, drafting, sketching etc.)

3. INDICATIVE CONTENT

Unit 1

Introduction: Introduction to Databases Security, Problems in Databases Security Controls,
Security Models – 1: Introduction, Access Matrix Model ,Take-Grant Mode! , Aclcn Model, PN Model, Hartsor and Hsiao's Model, Fernandez's Model, Bussolati and Martella's Model for Distributed databases
Security Models – 2: Bell and LaPadula's Model, Biba's Model, Dion's Model, Sea View
Model, Jajodia and Sandhu'r Model, The Lattice Model for the Flow Control, Conclusion

Unit 2

Security Mechanisms: Introduction, User Identification/Authentication, Memory Protection, Resource Protection Control , Flow Mechanisms, Isolation Security, Functionalities in Some Operating Systems Trusted Computer System, Evaluation Criteria - Security Software Design: Introduction, A Methodological Approach to Security Software Design, Secure Operating System ,Design Secure DBMS, Design Security Packages, Database Security Design

Unit 3

Statistical Database Protection & Intrusion Detection Systems: Introduction, Statistics Concepts and Definitions, Types of Attacks, Inference Controls evaluation, Criteria for Control Comparison. IDES System, RETISS System, ASES System Discovery

Unit 4

Models for the protection of new generation Database Systems -1: Introduction, A Model for the protection of frame based systems , A Model for the protection of object: Oriented Systems SORION, Model for the protection of Object-Oriented Databases, Models for the protection of new Generation Database Systems -2: A Model for the protection of New Generation Database Systems: the Orion model Jajodia anc Kenan’s Model, A Model for the Protection of Active Databases, Conclusions

4.  LEARNING AND TEACHING STRATEGY

The module will be delivered through lectures and tutorial sessions by the use of step-by-step worked examples. The project development will be undertaken during IT laboratory sessions. Tutorials and IT labs will be also used to form their practical knowledge and professional skills.
Organized discussions and teamwork will help students to get cognitive, intellectual and key (transferable) skills.

5. ASSESSMENT STRATEGY

Assessment on the programme is undertaken in accordance with the current Academic Regulations of the Institute.
Assessment Criteria:
· For the examination setting and marking, schemes will be drawn as appropriate to the skills assessed.
· For the assessment of the laboratory work, the appropriate Laboratory assessment criteria will be used
· For the assignment, criteria will be drawn up appropriate to the topic, based on the generic marking criteria

6. STRATEGY FOR FEEDBACK AND STUDENT SUPPORT DURING MODULE

· Interactive lecturing style, with opportunities for questions, and requirement to work on simple problems with practical laboratory exercise also.
· Peer marking of tutorial questions for formative feedback.
· Tutorial classes where students can ask questions and be lead through solutions as required.
· Marked summative assessments (laboratory report and assignment) handed back to students, with comments.

7. INDICATIVE RESOURCES

•  Database Security by Castano, Silvana; Fugini, Maria Grazia; Martella, Giancarlo, Pearson Edition, 1994
•  Database Security and Auditing: Protecting Data Integrity and Accessibility 1st Edition, Hassan Afyouni Thomos Edition, 2006
•  Online materials uploaded on the Learning Portal
•  Background Texts (include number in library or URL)
•  Journals
•  Key websites and on-line resources
•  Teaching/Technical Assistance
•  Laboratory space and equipment
•  Computer requirements

8. TEACHING TEAM :

MRS.ALPHONSINE MUKABUNANI

This course will examine the aspects of electronic commerce. Topics include internet development, EDS, security, network connectivity and privacy. Basic business practices using electronic commerce will also be covered. This course covers tools, skills, business concepts, and social issues that surround the emergence of electronic commerce. The student will develop an understanding of the current practices and
opportunities in EDI, electronic publishing, electronic shopping, electronic distribution, electronic collaboration and database issues. Other issues include standards, security, authentication, privacy, intellectual property, acceptable use, legal liability, and
economic analysis.

This course is aimed to providing an understanding of the notions of project management. It also provides insight to various project functions including planning, organizing, staffing, directing and controlling. The course also introduces the various approaches to handle project risks.

The introduction discusses software engineering concepts and then after the course  dwells more on software project management.

Forensic and criminal psychology is an applied area of psychology that draws on other areas of psychological enquiry and explores the borderland between psychology and the law. The aim of this course is to provide the platform for students to provide an academic overview of some areas of Forensic and Criminal Psychology, while reinforcing previously learnt generic and specific academic skills.

1. COURSE SUMMARY

This module is intended to impart the learners the modern concepts of data mining and data ware housing with good practical skills. The automated extraction of hidden predictive information from databases can be done using the special software tools included in the lab work. Learners will also be trained to be familiar and skilled in existing software.

2. Learning Outcomes

A. Knowledge and Understanding

    Having successfully completed the module, students should be able to demonstrate  knowledge and understanding of:

1. Understand the basic concepts of data mining
2. Preprocess the data for mining applications
3. Have a basic knowledge on data warehouse and OLAP technology
4. Apply the association rules for mining the data
5. Design and deploy appropriate classification techniques
6. Cluster the high dimensional data for better organization of the data and Be able to detect anomalies from data

B. Cognitive/Intellectual skills/Application of Knowledge

     Having successfully completed the module, students should be able to:
     1-select relevant statistical methods for modelling data bases
     2-use data mining principle in development of solutions to specific computing problems involving enormous data
     3-apply knowledge and computing standards of Data warehousing to produce novel designs of software systems and data mining components
     4-critically assess design and research work done by other software professionals
     5-analyse failure in Data warehousing and take preventive measures

C. Communication/ICT/Numeracy/Analytic Techniques/Practical Skills

Having successfully completed the module, students should be able to:
    1-plan, manage conduct and report software research projects in data mining
    2-prepare technical report and deliver technical presentations on software Development/testing using data mining techniques
   3-Develop standards for Data warehousing and data mining software
   4-crtically asses research work done on Data manipulation
   5- Detect Data base failures and devise solutions
   6-demostrate practical applications of data mining

D. General transferable skills

Having successfully completed the module, students should be able to:
    1-Do life-long research on data
    2-Efficiently manage time and human resources in the manipulation of data
    3-Communicate effectively with other skilled data mining professionals/experts
    4-demonstrate numerical skills and problem solving techniques with new research work

3. INDICATIVE CONTENT

Data Mining: Introduction, Data preprocessing, Classification, Decision trees, Bayesian, Rulebased classification, Back propagation, Evaluating, Ensemble, KNN, Clustering, Partitioning, Hierarchical clustering, Density-based methods, Cluster evaluation, Association rule mining, Apriori, FP-growth, Eclat, , Web mining Applications of data mining , Data ,mining softwares. Case studies on WEKA, TANAGRA and similar softwares.

Data Warehousing concept: Definition Operational Data, Common Characteristics of Data Warehouse, Knowledge discovery and Decision Making, Knowledge discovery and Data Mining, Application of Data Warehouse.

Find User Data Access Tools: Data Warehouse Query Tools, Data Modeling Strategy – Star schema, Multi Fact Table Star Schema, Star with the Original Entry Relationship Model, Dimensional Model, OLAP, Relational OLAP, Multidimensional Database, Data Cube presentation of Fact Tables.

Data Warehouse, Architecture and Optimization: 3 Tier Architecture, Components of Warehouse, Classical Data Warehouse, Transportation of Data into the Data Warehouse, Data created in the Data Warehouse, Presentation of Data to End Users, Object Oriented System Architecture Definitions, Object Modeling Techniques. Implementing of the Application Design, Necessity of Data warehouse Metadata, Performance optimization, Data administration techniques.

4. LEARNING AND TEACHING STRATEGY

The module will be delivered through lectures, tutorial/practice sessions and group discussions.
In addition to the taught element, students will be expected to undertake practical case studies and do a mini project.

5. ASSESSMENT STRATEGY

Assessment on the programme is undertaken in accordance with the current Academic Regulations of the Institute.
Assessment Criteria:

•  For the examination setting and marking the UR-CST generic marking criteria will be used.
• For the assessment of the laboratory work, the CE&IT Laboratory assessment criteria will be used
•  For the assignment, criteria will be drawn up appropriate to the topic, based on the UR-CST generic marking criteria

6. STRATEGY FOR FEEDBACK AND STUDENT SUPPORT DURING MODULE

•  Interactive lecturing style, with opportunities for questions, and requirement to work on simple problems.
•  Peer marking of tutorial questions for formative feedback.
• Tutorial classes where students can ask questions and be lead through solutions as required.
•  Marked summative assessments (laboratory report and assignment) handed back to students, with comments.
•  Opportunities to consult lecturer and/or tutorial assistant in office hours.

7. INDICATIVE RESOURCES

• Jiawei Han and Micheline Kamber. (2011). Data Mining: Concepts and Techniques, Third Edition
• Thomas C. Hammergren. (2009).Data Warehousing For Dummies
•  Daniel T. Larose and Chantal D. Larose. (2015).Data Mining and Predictive Analytics
•  Online materials uploaded on the Learning Portal
•  Background Texts (include number in library or URL)
•  Journals8.

8. TEACHING TEAM :

Mrs. ALPHONSINE MUKABUNANI