Cheating reportedly affects most of the multi-player online games and might easily jeopardize the game experience by providing an unfair competitive advantage to one player over the others. Accordingly, several efforts have been made in the past years to find reliable and scalable approaches to solve this problem. Unfortunately, cheating behaviors are rather difficult to detect and existing approaches generally require human supervision. In this work we introduce a novel framework to automatically detect cheating behaviors in Unreal Tournament III by exploiting supervised learning techniques. Our framework consists of three main components: (i) an extended game-server responsible for collecting the game data; (ii) a processing backend in charge of preprocessing data and detecting the cheating behaviors; (iii) an analysis frontend. We validated our framework with an experimental analysis which involved three human players, three game maps and five different supervised learning techniques, i.e., decision trees, Naive Bayes, random forest, neural networks, support vector machines. The results show that all the supervised learning techniques are able to classify correctly almost 90% of the test examples.
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