Bowei Xi, "A Game Theoretic Approach for Adversarial Machine Learning -- When Big Data Meets Cyber Security"

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.