Detecting-Cheating-In-FPS-Online-Games-Using-GPT-Technologies

Outliers in FPS games are often indicative of cheating behaviors, posing a significant challenge to ensuring fair gameplay. This study explores the application of various outlier detection techniques, including Mahalanobis distance, Principal Component Analysis (PCA), K-Nearest Neighbors (KNN), Isolation Forest, and Local Outlier Factor (LOF), to identify potential cheaters in FPS games. A comprehensive analysis is conducted using a dataset of FPS game sessions, and the performance of each technique is evaluated using accuracy, precision, recall, and other relevant metrics. The study highlights the potential of advanced AI technologies, such as GPT, in enhancing outlier detection techniques to improve gameplay and effectively detect cheating in FPS games.