Introduction: Introduction to Information Security, Introduction to Data Mining for Information Security
Network Intrusion Detection: Signature-based solutions (Snort, etc), Data-mining-based solutions (supervised and unsupervised); Deep Packet Inspection: Alert aggregation for web security, One-class Multi-classifier systems for packet payload modeling and network intrusion detection , Host Intrusion Detection: Analysis of shell command sequences, system call sequences, and audit trails, Introduction to Insider threats, Masquerader/Impersonator/Insider threat detection strategies, Web Security: Anomaly detection of web-based attacks using web server logs, Anomaly detection in web proxy logs Email: Spam detection, Phishing email detection, phishing website detection Social network security: Detecting compromised accounts, detecting social network spam, Authentication: Anomaly detection of Single Sign On (Kerberos, Active Directory), Detecting Pass-the-Hash and Pass-the-Ticket attacks, Behavioural Biometrics: Active authentication using behavioural and cognitive biometrics, Mouse dynamics analysis for active authentication, touch and swipe pattern analysis for mobile active authentication, Automated correlation: Attack trees, Building attack scenarios from individual alerts, Issues: Privacy issues, Adversarial machine learning: Overview of Multi-classifier systems (MCS), advantages of MCS in security analytics, security of machine learning, Other potential topics: Fraud detection, IoT/Infrastructure security, Mobile/Wireless security, Machine Learning for Security: Challenges in applying machine learning (ML) to security, guidelines for applying ML to security, Current and future trends in security.