Bowei Xi - Purdue University

Feb 27, 2019

Size: 230.4MB

Download: Video Icon MP4 Video  
Watch in your Browser   Watch on Youtube Watch on YouTube

Abstract

Nowadays more and more data are gathered for detecting and
preventing cyber attacks. Unique to the cyber security
applications, learning models face active adversaries that try to
deceive learning models and avoid being detected. Hence future
datasets and the training data no longer follow the same
distribution. The existence of such adversarial samples
motivates the development of robust and resilient adversarial
learning techniques. Game theory offers a suitable framework to
model the conflict between adversaries and defender. We develop a
game theoretic framework to model the sequential actions of the
adversaries and the defender, allowing players to maximize their
own utilities. For supervised learning tasks, our adversarial
support vector machine has a conservative decision boundary,
whereas our robust deep neural network plays a random strategy
inspired by the mixed equilibrium strategy. One the other hand,
in real practice, labeling the data instances often requires
costly and time-consuming human expertise and becomes a
significant bottleneck. We develop a novel grid based adversarial
clustering algorithm, to understand adversaries' behavior from a
large number of unlabeled instances. Our adversarial clustering
algorithm is able to identify the normal regions inside mixed
clusters, and to draw defensive walls around the center of the normal
objects utilizing game theoretic ideas. Our algorithm also
identifies sub-clusters of adversarial samples and the overlapping areas
within mixed clusters, and identify outliers which may be

potential anomalies.

Unless otherwise noted, the security seminar is held on Wednesdays at 4:30P.M. STEW G52 (Suite 050B), West Lafayette Campus. More information...

Coming Up!

Our annual security symposium will take place on April 7 & 8, 2020.
Purdue University, West Lafayette, IN

More Information