Basics of pattern recognition - Design principles of pattern recognition system- Learning and adaptation- Pattern recognition approaches
Introduction-Bayesian Decision Theory Continuous Features-Minimum error rate- classification- classifiers, discriminant functions, and decision surfaces; The normal density- Discriminant functions for the normal density Maximum likelihood estimation.
Density Estimation - Parzen Windows - K-Nearest Neighbor Estimation - Nearest Neighbor Rule- Fuzzy clustering. NonMetric Methods- Introduction-Decision Trees- CART- Other Tree Methods Recognition with Strings-Netflow analyzer.
Anomaly detection– data driven methods – feature engineering – detection with data and algorithms – challenges using ML- response and mitigation – Malware Analysis: defining – feature generation – classification - Network Traffic Analysis.
Facing the Cybercrime Problem Head-on- Emerging Cybercrime Techniques- Understanding the People on the Scene- The Computer Investigation Process- Acquiring Data, Duplicating Data, and Recovering Deleted Files- Understanding Network Intrusions and Attacks.
Reference Book:
Richard O. Duda, Peter E. Hart and David G. Stork, “Pattern Classificationâ€, 2nd Edition, John Wiley, 2006. “A Brief History of Cyber Crime†written by: R. Elizabeth C. Kitchen edited by: M.S. Smith, 2010. Homayoon Beigi ,Fundamentals of Speaker Recognition, Springer,2011
Text Book:
Abhijit S. Theodoridis and K. Koutroumbas, “Pattern Recognitionâ€, 4th Ed, Academic Press, 2009. Clarence Chio David Freeman “Machine Learning and Security: Protecting Systems with Data and Algorithmsâ€,& quot; Reilly Media, Inc.& quot;, 2018 109 “Scene of the Cybercrime†2nd Edition by Debra Littlejohn Shinder, Michael Cross, 2002. Earl Gose, Richard Johnsonbaugh, Steve Jost- “Pattern Recognition and Image Analysis†– Pearson Education, 2007.
