  Nowadays more and more data are gathered for detecting and preventing cyber
attacks. In cyber security applications, data analytics techniques have to deal
with active adversaries that try to deceive the data analytics models and avoid
being detected. The existence of such adversarial behavior motivates the
development of robust and resilient adversarial learning techniques for various
tasks. Most of the previous work focused on adversarial classification
techniques, which assumed the existence of a reasonably large amount of
carefully labeled data instances. However, in practice, labeling the data
instances often requires costly and time-consuming human expertise and becomes
a significant bottleneck. Meanwhile, a large number of unlabeled instances can
also be used to understand the adversaries' behavior. To address the above
mentioned challenges, in this paper, we develop a novel grid based adversarial
clustering algorithm. Our adversarial clustering algorithm is able to identify
the core normal regions, and to draw defensive walls around the centers of the
normal objects utilizing game theoretic ideas. Our algorithm also identifies
sub-clusters of attack objects, the overlapping areas within clusters, and
outliers which may be potential anomalies.
