You Are Not My Teammate: Behavioral Fingerprint-based Detection of Suspicious Account Misuse
Online games have been continuously affected by cyber threats such as game bots and gold farming. Game bots, which are automated programs that play on behalf of human users, significantly accelerate character progression and reduce the engagement of legitimate players, potentially leading to user churn. In addition, gold farming enables the monetization of in-game currency into real-world money, resulting in unfair profits. For these reasons, prior studies have primarily focused on detecting game bots and gold farming. However, in competitive Multiplayer Online Battle Arena (MOBA) games such as League of Legends, match outcomes and rankings are the primary objectives, where individual performance is more critical than in-game economic factors. Accordingly, account misuse such as account sharing and boosting has emerged as a major threat to fair competition. In this study, we propose a behavioral fingerprint-based detection method. Our approach analyzes and quantifies changes between a player's historical and recent in-game behaviors. Consequently, it enables the robust identification of suspicious account sharing and boosting, even in label-scarce environments. Experimental results show that behavioral fingerprints within the same account are distinguishable from those across different accounts, supporting rapid detection of suspicious account misuse even with limited labeled data.