• Journal of Korea Game Society
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• v.15 no.3
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• pp.177-188
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• 2015

In-game cheating by the use of unauthorized software programs has always been a big problem that they can damage in First Person Shooting games, although companies operate a variety of client security solutions in order to prevent games from the cheating attempts. This paper proposes a method for detecting cheaters in FPS games by using game log analysis in a server-side. To accomplish this, we did a comparative analysis of characteristics between cheaters and general users focused on commonly loaded logs in the game. We proposed a cheating detection model by using artificial neural network algorithm. In addition, we did the performance evaluation of the proposed model by using the real dataset used in business.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1465-1475
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• 2015

Cheating detection models in the online games can be divided into two parts. The one is on client based model, which is designed to detect malicious programs not to be run while playing the games. The other one is server based model, which distinguishes the difference between benign users and cheaters by the server log analysis. The client based model provides various features to prevent games from cheating, For instance, Anti-reversing, memory manipulation and so on. However, being deployed and operated on the client side is a huge weak point as cheaters can analyze and bypass the detection features. That Is why the server based model is an emerging way to detect cheating users in online games. But the simple log data such as FPS's one can be hard to find validate difference between two of them. In this paper, In order to compensate for the disadvantages of the two detection model above, We use the existing game security solution log as well as the server one to bring high performance as well as detection ratio compared to the existing detection models in the market.