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Behavioral-based cheating detection in online first person shooters using machine learning techniques.
| Content Provider | CiteSeerX |
|---|---|
| Author | Alayed, Hashem Frangoudes, Fotos Neuman, Clifford |
| Abstract | Abstract—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 serverside 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. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Behavioral-based Cheating Detection Online First Person Shooter Machine Learning Technique Different Option Detection Measure High Accuracy Online Game Useful Suggestion Player Behavior Many Consequence Detect Cheater Different Organization Serverside Anti-cheating Solution Cheating Model Method Result Several Anti-cheating Solution Abstract Cheating Important Part Online Game Developer Detailed Analysis User Privacy Commercial Online Game Game Log |
| Content Type | Text |
