🤖 AI Summary
This study addresses the rampant proliferation of aimbot cheats in first-person shooter (FPS) games by proposing YAACS, a deep learning–based server-side detection system. YAACS models sequential player behavioral data—including aiming velocity, firing frequency, and target distance—and innovatively introduces contextualized temporal sequences into anti-cheat mechanisms to effectively discriminate between legitimate and cheating behaviors. The method employs a stacked LSTM architecture followed by dense layers to process 128-tick action sequences and integrates a parser and middleware for seamless deployment within game servers. Experimental results demonstrate that YAACS achieves a classification accuracy of 88.6% with a false positive rate of only 0.97%, substantially outperforming a decision tree baseline (2.68% false positives), thereby enhancing both competitive fairness and player experience without compromising detection precision.
📝 Abstract
Modern video games are becoming more complex day by day. Most of these modern games are multiplayer first-person shooter (FPS) games. The rising popularity of FPS games emphasizes the need to combat cheating for fair and enjoyable gaming. As the number of players using cheating techniques like aimbots, wallhacks, and speed hacks is also increasing, we need a way to detect players who are using cheating tools to gain an unfair advantage over regular players. In this system, we focus exclusively on detecting aimbot cheats. Players who use aimbot cheats generally do not prioritize other aspects of the game. To distinguish between regular and cheating players, we identify specific features encompassing time-series data such as aim velocity, number of shots, distance to target, and more, along with behavioral data such as utility usage, player movement, and other gameplay patterns. Utilizing these features, we construct a server-side aimbot detection classifier named 'YAACS'. YAACS comprises a parser, a deep learning model, and intermediary connection utilities designed for integration with the game server. The proposed system achieves a classification accuracy of 88.6% with a false positive rate of 0.97% using a Stacked LSTM with Dense layers trained on sequences of 128 ticks (Tick Delta Negative=56, Tick Delta Positive=24), outperforming the Decision Tree baseline which achieves a higher accuracy of 96.2% but at a false positive rate of 2.68%, 2.76x worse than the best LSTM configuration. These results demonstrate that incorporating temporal context through sequence modelling is critical for minimising false accusations in FPS cheat detection.