Detecting Game Bots with Encrypted Game Traffic

Y. Lu, Y. Zhu, M. Itomlenskis, S. Vyaghri, and H. Fu (USA)


Game Bot, MMORPG


In this paper, we propose an approach to detect cheat ing with game bots in Massively Multiplayer Online Role playing Games (MMORPGs). Cheating with game bots which can auto-play online games without human involve ment is a big threat to the industry of MMORPGs. The proposed approach can detect game bots by analysis of en crypted game traffic. In the proposed detection, hidden Markov models (HMMs), known for the power in tempo ral pattern recognition, are trained to model game bots’ and human players’ gaming behaviors. A detection decision is made by testing a game trace of interest against trained HMMs. We evaluate the proposed detection approach with game traces collected from the Internet. Our experiment re sults show that the proposed detection can detect game bots accurately with only a small number of training traces.

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