JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• 1. Discuss the overall process of how data analytics is applied
• 2. Discuss how data analytics can be used to better address and identify risks
• 3. Help mitigate risks from fraud and waste for our clients and organizations

Course Outcomes:

• 1. Formulate reasons for using data analysis to detect fraud.
• 2. Explain characteristics and components of the data and assess its completeness.
• 3. Identify known fraud symptoms and use digital analysis to identify unknown fraud symptoms.
• 4. Automate the detection process.
• 5. Verify results and understand how to prosecute fraud

UNIT - I

Introduction: Defining Fraud, Anomalies versus, Fraud, Types of Fraud, Assess the Risk of Fraud, Fraud Detection, Recognizing Fraud, Data Mining versus Data Analysis and Analytics, Data Analytical Software, Anomalies versus Fraud within Data, Fraudulent Data Inclusions and Deletions

UNIT - II

The Data Analysis Cycle, Evaluation and Analysis, Obtaining Data Files, Performing the Audit, File Format Types, Preparation for Data Analysis, Arranging and Organizing Data, Statistics and Sampling, Descriptive Statistics, Inferential Statistics

UNIT - III

Data Analytical Tests: Benford’s Law, Number Duplication Test, Z-Score, Relative Size Factor Test, Same-Same-Same Test, Same-Same-Different Test

UNIT - IV

Advanced Data Analytical Tests Correlation, Trend Analysis, GEL-1 and GEL-2, Skimming and Cash Larceny, Billing schemes: and Data Familiarization, Benford’s Law Tests, Relative Size Factor Test, Match Employee Address to Supplier data

UNIT - V

Payroll Fraud, Expense Reimbursement Schemes, Register disbursement schemes

TEXT BOOK:

• 1. Fraud and Fraud Detection: A Data Analytics Approach by Sunder Gee, Wiley

REFERENCES:

• 1. Blokdyk Gerardus, Data analysis techniques for fraud detection, Createspace Independent Publishing Platform
• 2. Leonard W. Vona, Fraud Data Analytics Methodology: The Fraud Scenario Approach to Uncovering Fraud in Core Business Systems, Wiley WEB & DATABASE SECURITY LAB (Lab - III) M.Tech CNIS/CN/CYS I Year II Sem. L T P C 0 0 4 2

Pre-requisites

Database Management Systems, Practical exposure on Commercial Database

Management Systems, Web Security,

Course Outcomes

At the end of the course the student will be able to:

• 1. Design of access control methods for secure web & database application development
• 2. Analyse and Classify the vulnerabilities in the Web and Database applications.
• 3. Design & implementation various methods for web & database intrusion detection.
• 4. Design and Implementation security audit methods.

List of Experiments:

• 1. Creation and manipulation of database using SQL scripts and graphical interfaces.
• 2. Implementing DAC: Implementation of database security policies using DAC in oracle 10g/SQL server
• 3. Implementing of MAC to ensure confidentiality and control information flow using either Oracle 10g or SQL server. This provides exposure to understand the concepts of MAC and Trojan hose
• 4. Implementation of Virtual Private Database using View using Oracle 10g or SQL server
• 5. Design a method to simulate the HTML injections and cross-site scripting (XSS) to exploit the attackers.
• 6. Determine HTML injection bugs and possible measures to prevent HTML injection exploits.
• 7. Implement Secure coding for buffer flow heap attacks.
• 8. Implementation of Design methods to break authentication schemes
• 9. Implementation of methods for abusing Design Deficiencies against web sites