Description:

Adversarial Machine Learning - Big Data Meets Cyber Security

Time: 11:00 a.m.

Speaker: Bowei Xi, Associate Professor of Statistics, Department of Statistics, Purdue University

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road

Abstract: As more and more cyber security incident data ranging from systems logs to vulnerability scan results are collected, machine learning techniques are becoming an essential tool for real-world cyber security applications. One of the most important differences between cyber security and many other applications is the existence of malicious adversaries that actively adapt their behavior to make the existing learning models ineffective. Unfortunately, traditional learning techniques are insufficient to handle such adversarial problems directly. The adversaries adapt to the defender's reactions, and learning algorithms constructed based on the current training dataset degrades quickly. To address these concerns, we develop a game theoretic framework to model the sequential actions of the adversary and the defender, while both parties try to maximize their utilities. We also develop an adversarial support vector machine method and an adversarial clustering algorithm to defend against active adversaries.