Aim-assist cheats are the most prevalent and infamous form of cheating in First-Person Shooter (FPS) games, which help cheaters illegally reveal the opponent's location and auto-aim and shoot, and thereby pose significant threats to the game industry. Although a considerable research effort has been made to automatically detect aim-assist cheats, existing works suffer from unreliable frameworks, limited generalizability, high overhead, low detection performance, and a lack of explainability of detection results. In this paper, we propose XGuardian, a server-side generalized and explainable system for detecting aim-assist cheats to overcome these limitations. It requires only two raw data inputs, pitch and yaw, which are all FPS games' must-haves, to construct novel temporal features and describe aim trajectories, which are essential for distinguishing cheaters and normal players. XGuardian is evaluated with the latest mainstream FPS game CS2, and validates its generalizability with another two different games. It achieves high detection performance and low overhead compared to prior works across different games with real-world and large-scale datasets, demonstrating wide generalizability and high effectiveness. It is able to justify its predictions and thereby shorten the ban cycle. We make XGuardian as well as our datasets publicly available.

翻译：瞄准辅助作弊是第一人称射击（FPS）游戏中最普遍且臭名昭著的作弊形式，它通过非法揭示对手位置、自动瞄准与射击来帮助作弊者，从而对游戏产业构成重大威胁。尽管已有大量研究致力于自动检测瞄准辅助作弊，但现有工作普遍存在框架不可靠、泛化能力有限、开销高、检测性能低以及检测结果缺乏可解释性等问题。本文提出XGuardian，一种服务器端的泛化可解释系统，用于检测瞄准辅助作弊以克服上述局限。该系统仅需俯仰角与偏航角两种原始数据输入（所有FPS游戏必备数据），即可构建新颖的时序特征并描述瞄准轨迹，这些特征是区分作弊者与正常玩家的关键。XGuardian基于最新主流FPS游戏《CS2》进行评估，并在另外两款不同游戏中验证了其泛化能力。在真实世界大规模数据集上的跨游戏测试表明，相较于现有研究，该系统实现了更高的检测性能与更低的开销，展现出广泛的泛化性与高效性。该系统能够为其预测提供合理解释，从而缩短封禁处理周期。我们已将XGuardian及相关数据集公开。