Deep Learning Anti-Cheat Detects Aimbots in FPS Games.

The increasing popularity of multiplayer first-person shooter (FPS) games has led to a rise in cheating, particularly aimbots, which undermine fair play. To combat this, researchers have developed 'YAACS', a new server-side anti-cheat system specifically designed for aimbot detection using a combination of deep learning and machine learning. YAACS analyzes various in-game data points, including time-series data like aim velocity and distance to target, as well as behavioral patterns such as utility usage and player movement. These features help differentiate legitimate players from those using aimbots, who often neglect other gameplay aspects. The system employs a Stacked LSTM with Dense layers, trained on sequences of game ticks, achieving an 88.6% classification accuracy. Crucially, it maintains a very low false positive rate of 0.97%, which is vital to avoid wrongly accusing legitimate players. This performance significantly surpasses a Decision Tree baseline, which, despite higher accuracy, had a much higher false positive rate, highlighting the importance of temporal context in minimizing false accusations.