• 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.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.81-92
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• 2013

It is important to construct a firm basis for accusing potential violators of academic integrity in order to avoid spurious accusations and false convictions. Educational researchers have developed many statistical methods that can either uncover or confirm cases of cheating on tests. However, most of them rely on simple correlation-based measures, and often fail to account for patterns in responses or answers. In this paper, we propose a new statistical index denoted by a Standardized Signed Entropy Similarity Score to resolve this difficulty. In addition, we apply the proposed method to analyze a real data set and compare the results to other existing methods.