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Illicit Bits: Detecting and Analyzing Contraband Networks in Massively Multiplayer Online Games

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6 Author(s)
Muhammad Aurangzeb Ahmad ; Dept. of Comput. Sci., Univ. of Minnesota, Minneapolis, MN, USA ; Brian Keegan ; Sophia Sullivan ; Dmitri Williams
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Although trade in illicit items and services is prevalent in many economic systems, collecting reliable data and making empirical claims about this activity is difficult. Using anonymized behavioral logs from a massively multiplayer online game, we analyze the items exchanged by players later banned for gold farming. We simultaneously analyze clandestine social networks of deviant players in MMOGs as well the network of contraband items that are sold by these players. The insights from the network analysis are used to build predictive models for identifying deviant players in the clandestine networks. We show that the results obtained from our proposed approach are far superior to the state of the art for such clandestine networks. Additionally we observed that the contraband networks contain certain type of objects which are not found in their "normal" counterparts.

Published in:

Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on

Date of Conference:

9-11 Oct. 2011