The gaming industry has experienced substantial growth, but cheating in online games poses a significant threat to the integrity of the gaming experience. Cheating, particularly in first-person shooter (FPS) games, can lead to substantial losses for the game industry. Existing anti-cheat solutions have limitations, such as client-side hardware constraints, security risks, server-side unreliable methods, and both-sides suffer from a lack of comprehensive real-world datasets. To address these limitations, the paper proposes HAWK, a server-side FPS anti-cheat framework for the popular game CS:GO. HAWK utilizes machine learning techniques to mimic human experts' identification process, leverages novel multi-view features, and it is equipped with a well-defined workflow. The authors evaluate HAWK with the first large and real-world datasets containing multiple cheat types and cheating sophistication, and it exhibits promising efficiency and acceptable overheads, shorter ban times compared to the in-use anti-cheat, a significant reduction in manual labor, and the ability to capture cheaters who evaded official inspections.

翻译：游戏产业经历了显著增长，但网络游戏中的作弊行为对游戏体验的完整性构成了严重威胁。作弊行为，特别是在第一人称射击（FPS）游戏中，可能导致游戏产业遭受重大损失。现有反作弊解决方案存在局限性，例如客户端硬件限制、安全风险、服务器端方法不可靠，以及双方均缺乏全面的真实世界数据集。为应对这些局限，本文提出了HAWK，一个针对热门游戏CS:GO的服务器端FPS反作弊框架。HAWK利用机器学习技术模拟人类专家的识别过程，采用新颖的多视角特征，并配备了清晰定义的工作流程。作者使用首个包含多种作弊类型和复杂作弊手段的大规模真实世界数据集对HAWK进行评估，结果显示其具有良好效率与可接受的开销、相比现行反作弊系统更短的封禁时间、显著减少的人工工作量，并能捕获规避官方检测的作弊者。