Detecting Cheaters for Multiplayer Games: Theory,

Massively multiplayer game holds a huge market in the digital entertainment industry. Companies invest heavily in the game and graphics development since a successful online game can attract million of users, and this translates to a huge investment payoff. However, multiplayer online game is 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 robot to play the game automatically and gain an unfair advantage. Currently, some popular online games release software patches 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 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 propose method, we implement a prototype multiplayer game system and to detect whether a player is using the "aiming robot" for cheating or not. Experiments show that not only we can effectively detect cheaters, but the false positive rate is extremely low. 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 multiplayer games