Estimation of In-Game Player Behavior by Measuring Key Gameplay Parameters

Julia Orlova, Anton Stepanov, Anton Vinogradov, Lubov Orlova, Anna Baldycheva, Andrey Somov

Abstract: In this work, we address the cheating problem in video games and provide instruments for reliable detection of various cheaters based on their in-game behavior in the most popular FPS game Counter-Strike: Global Offensive. For this purpose, we collect more than 2,243 competitive game records through official Valve servers. This corresponds to more than 14,000 players with various gaming skill levels. Thorough application of mathematical processing methods, as well as the eSports expert assistance, allows us to develop meaningful and accurate metrics for measuring in-game player actions. We report that with their help cheater detection has high accuracy of 85%, and outperform VAC, VACNet, and OverWatch in a comparative analysis. The tools are additionally tested on the professional players and show a low false positive rate.