Player authentication is important to ensure fairness in esports, and fighting games present a particularly challenging setting because of their discrete and limited input modalities and rapidly changing gameplay situations. While prior work has demonstrated the feasibility of controller-operation-based authentication in this domain, it has evaluated the approach only on a single title, leaving the empirical basis for this line of research still narrow. In this paper, using replay data from GUILTY GEAR -STRIVE-, we present a method for constructing a dataset for player authentication, which integrates three types of information: controller operation logs, battle progression, and battle attributes. To demonstrate its utility, we apply an existing controller-operation-based authentication method and show that the dataset supports the evaluation of such methods. We also analyze the relationship between player proficiency and authentication performance, and show that authentication tends to become more difficult as player proficiency increases. Our results show replay-derived data are practical for evaluating and analyzing player authentication in fighting 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.