Fair Play and Identity: In-Game Behavioral Biometrics for Player Identification in Competitive Online Games

As one of the largest entertainment sectors globally, the gaming industry has already surpassed the music industry in revenue and is expected to continue its steady growth in the years ahead. Beyond leisure, gaming has become a competitive arena, particularly with the rise of esports, where games like CounterStrike (CS) draws millions of players and viewers worldwide. With the expansion of online gaming and virtual environments, new security challenges emerge that require advanced solutions. One promising approach is biometric identification based on ingame behavioral data, which is non-invasive and challenging to replicate. Although research has demonstrated effective player identification in Virtual Reality and turn-based games like chess, games like $C S$ introduce unique challenges due to their dynamic high-speed interactions. This study explores behavioral biometrics to identify esports players based on their in-game behaviors. We use a multiclass Random Forest Classifier to analyze in-game movement, positioning, and weapon choices to identify individual players accurately. Our models achieve up to 98 % accuracy based on a combined dataset of 360 players across 320 matches. This data is divided into eight separate datasets for each map. These findings contribute to behavior-based player biometrics and identification, with applications in player analytics, team strategy optimization, and security in competitive gaming.