When: Monday, May 12, 12:00 p.m.

Where: Distributed Education Center
Lobby LevelCollaborative Innovation Center

Alvaro Cardenas, Post-Doc
University of California at Berkeley

CyLab Seminar

Abstract:
The complexity and uncertainty of several computer and network security problems has motivated a significant amount of research in machine learning and statistical methods for intrusion detection. While these techniques appear suitable for several problems, they were not designed to provide formal performance guarantees against intelligent and adaptive adversaries. In this talk I will present our efforts for modeling and interpreting statistical detection and correlation rules for detecting attackers. In particular we present new metrics for evaluating intrusion detection systems, a game-theoretic formulation of an adaptive attacker against an intrusion detection system, and a new correlation rule for combining alerts from multiple sensors.

I will finish my talk with some recent and future work on the security of control systems, cybercrime and economic incentives for security.

Alvaro Cardenas is a postdoctoral scholar at the University of California, Berkeley, where he is a member of the Team for Research in Ubiquitous Secure Technology (TRUST). He obtained his MS and Ph.D. degrees from the University of Maryland, College Park. His research interests center around the interface between information security and formal analytical techniques in probability theory, information theory, game theory and machine learning.

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