Behavioral-based cheating detection in online first person shooters using machine learning techniques

Cheating in online games comes with many consequences for both players and companies. Therefore, cheating detection and prevention is an important part of developing a commercial online game. Several anti-cheating solutions have been developed by gaming companies. However, most of these companies use cheating detection measures that may involve breaches to users' privacy. In our paper, we provide a server-side anti-cheating solution that uses only game logs. Our method is based on defining an honest player's behavior and cheaters' behavior first. After that, using machine learning classifiers to train cheating models, then detect cheaters. We presented our results in different organizations to show different options for developers, and our methods' results gave a very high accuracy in most of the cases. Finally, we provided a detailed analysis of our results with some useful suggestions for online games developers.