Dynamic Bayesian approach for detecting cheats in multi-player online games

Massively multi-player games hold a huge market in the digital entertainment industry. Companies invest heavily in game developments since a successful online game can attract millions of users, and this translates to a huge investment payoff. However, multi-player online games are also subjected to various forms of "hacks" and "cheats". Hackers can alter the graphic rendering to reveal information otherwise be hidden in a normal game, or cheaters can use software robots to play the game automatically and thus gain an unfair advantage. To overcome these problems, some popular online games release software patches constantly to block "known" hacks or incorporate anti-cheating software to detect "known" cheats. This not only creates deployment difficulty but new cheats will still be able to breach the normal game logic until software patches or updates of the anti-cheating software are available. Moreover, the anti-cheating software themselves are also vulnerable to hacks. In this paper, we propose a "scalable" and "efficient" method to detect whether a player is cheating or not. The methodology is based on the dynamic Bayesian network approach. The detection framework relies solely on the game states and runs in the game server only. Therefore, it is invulnerable to hacks and it is a much more deployable solution. To demonstrate the effectiveness of the proposed method, we have implemented a prototype multi-player game system to detect whether a player is using any "aiming robot" for cheating or not. Experiments show that the proposed method can effectively detect cheaters on a first-person shooter game with extremely low false positive rate. We believe the proposed methodology and the prototype system provide a first step toward a systematic study of cheating detection and security research in the area of online multi-player games.