AI 中文总结
针对 MOBA 游戏中账户共享、代练等滥用威胁，提出基于行为指纹的检测方法，可在标签稀缺环境下利用有限标注数据实现快速稳健检测。
AI 中文摘要
在线游戏持续受到游戏机器人、金币 farming 等网络威胁影响。游戏机器人是代替人类用户进行游戏的自动化程序，会大幅加快角色升级，降低合法玩家的参与度，可能导致用户流失；金币 farming 可将游戏内货币兑换为现实货币获利，造成不公平收益。因此，过往研究主要聚焦于检测游戏机器人和金币 farming。但在《英雄联盟》等竞技类多人在线战术竞技（MOBA）游戏中，比赛结果和排名是核心目标，个人表现比游戏内经济因素更关键，账户共享、代练等账户滥用已成为公平竞争的主要威胁。本研究提出一种基于行为指纹的检测方法，该方法分析并量化玩家历史与近期游戏行为的变化，可在标签稀缺环境下稳健识别可疑账户共享与代练。实验结果显示，同一账户内的行为指纹与不同账户间的行为指纹可区分，即便使用有限标注数据也能快速检测可疑账户滥用。
英文摘要
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.
Comments12 pages, 4 figures. Accepted at WISA 2026
