Large-Scale Study of Controller Operation Dynamics for Player Authentication in Fighting Games

Abstract

Player authentication is crucial for preserving the integrity of esports competitions, yet current measures cannot entirely prevent impersonation via collusion. While behavioral biometrics offer a promising solution, their application has been largely limited to continuous input devices and homogeneous gameplay states. Taking fighting games as an ideal domain for addressing these gaps, we propose a method that applies keystroke-authentication principles to controller operation dynamics and introduce density-based segmentation to capture context-dependent operation patterns. Experimental results using data from 307 matches by 60 players in Street Fighter 6 demonstrate the effectiveness of our approach, achieving a PR-AUC of 55.7% and an EER of 12.5%. Further analysis revealed that, aggregating decisions using 30 seconds of controller operations, our method can achieve a PR-AUC higher than 90% and EER lower than 1%. These findings validate the applicability of keystroke-dynamics principles to controller dynamics and establish an interpretable baseline.