Download A. Prerequisites: Math courses: Linear Algebra, Discrete

A. Prerequisites:
1. Math courses: Linear Algebra, Discrete Mathematics, and Probability & Statistics.
2. Computer Science: CS3
3. Computer Science junior/senior or graduate student.
B. Module Contents (tentative) (9 hours)
1. Overview(1 hour)
 What Is Data Mining?
 The Origins of Data Mining
 Data Mining Tasks
 Types of Data
 Data Preprocessing
2. Association rule mining (1 hour)
 Frequent Itemset Generation
 Rule Generation
 Compact Representation of Frequent Itemsets (optional)
3. Classification(1 hour)
 Decision Tree
 Rule-Based Classifier (optional)
 Bayesian Classifiers
 Support Vector Machine (SVM)
4. Clustering(1 hour)
 K-means
 Agglomerative Hierarchical Clustering
 DBSCAN (optional)
 Cluster Evaluation(optional)
5. Anomaly detection(1.5 hours)
 Proximity-Based Outlier Detection
 Density-Based Outlier Detection
 Clustering-Based Techniques
6. Data Mining for Intrusion Detection (1.5 hours)
 Goals of Intrusion detection systems
 Data Mining framework for intrusion detection
 Applying Association mining in intrusion detection
 Building classifiers for intrusion detection
 Mining patterns from audit data(optional)
7. Application of data mining to computer forensics (1.5 hours)
 Background on computer forensics
 Digital evidence collection and analysis
 Fraud detection(optional)
 Financial Forensics(optional)
8. Application of data mining to emerging security issues (0.5 hours)
C. Homework Exercises and Quizzes:
2 Q/A exercises
D. Hands On Activities:
1. Use Digital Forensics Framework (http://www.digital-forensic.org/) to collect, preserve and
reveal digital evidences.
2. Use R to identify anomalies
E. References:
1. Applications of Data Mining in Computer Security, Barbara, D. and Jajodia, S., 9781402070549,
2002, Kluwer Academic
2. Chen, H., Chung, W., Qin, Y., Chau, M., Xu, J. J., Wang, G., Zheng, R., Atabakhsh, H. (2003).
Crime Data Mining: An Overview and Case Studies. ACM International Conference Proceeding
Series; Vol. 130, 1-5.
3. R and Data Mining: Examples and Case Studies, Y. Zhao, Academic Press, Elsevier, 2012, ISBN:
978-0-12-396963-7