像人类一样识别：下一代第一人称射击游戏反作弊框架的探索者
Identify As A Human Does: A Pathfinder of Next-Generation Anti-Cheat Framework for First-Person Shooter Games
AI总结：
针对FPS游戏作弊问题及现有反作弊方案的局限性，提出服务器端反作弊框架HAWK，通过模仿人类专家识别过程、采用多视图特征，在大型真实数据集上表现出高效低开销、封禁更快、减少人工且能捕获逃避检测作弊者的优势。
摘要：
游戏行业已实现大幅增长，但在线游戏中的作弊行为对游戏体验的完整性构成重大威胁。尤其是在第一人称射击（FPS）游戏中，作弊可能给游戏行业造成巨大损失。现有反作弊解决方案存在局限性，例如客户端硬件约束、安全风险、服务器端方法不可靠，且双方都缺乏全面的真实世界数据集。为解决这些局限性，本文提出HAWK——一个面向热门游戏CS:GO的服务器端FPS反作弊框架。HAWK利用机器学习技术模仿人类专家的识别过程，采用新颖的多视图特征，并配备明确的工作流程。作者使用首个包含多种作弊类型和作弊复杂度的大型真实世界数据集对HAWK进行评估，结果显示其具有良好的效率和可接受的开销，与在用反作弊系统相比封禁时间更短，大幅减少人工劳动，并能捕获逃避官方检测的作弊者。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
