AAAI Publications, Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence

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Towards Analyzing Adversarial Behavior in Clandestine Networks
Muhammad Aurangzeb Ahmad, Brian Keegan, Sophia Sullivan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor

Last modified: 2011-08-24

Abstract


Adversarial behavioral has been observed in many different contexts. In this paper we address the problem of adversarial behavior in the context of clandestine networks. We use data from a massively multiplayer online role playing game to illustrate the behavioral and structural signatures of deviant players change over time as a response to "policing" activities of the game administrators. Preliminary results show that the behavior of the deviant players and their affiliates show co-evolutionary behavior and the timespan within the game can be divided into different epochs based on their behaviors. Feature sets derived from these results can be used for better predictive machine learning models for detecting deviants in clandestine networks.

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