Machine Learning for Computer Security

Machine Learning for Computer Security

• Friday March 23, 10-12 A.M.
• Monday March 26, 3-5 P.M.
• Wednesday March 28, 2-4 P.M.
• Friday March 30, 10-12 A.M.

1. Introduction to intrusion detection. Slides.
   • Taxonomy: net/host
   • Host IDS: early approaches
   • Network IDS: feature extraction
   • Signature-based IDS:
2. Anomaly detection for network security. Slides.
   • Early approaches (packet headers, PAYL, Anagram)
   • Payload based approach
   • applications (ReMIND experiments, SCADA systems)
3. Classification for network security. Slides.
   • Early approaches
   • Why it is difficult to use classification for IDS
   • behavioral classification
   • automatic signature generation
4. Attacks against learning algorithms. Slides.
   • attack taxomony
   • attacks against anagram
   • attacks against automatic signature generation