用机器学习模拟真人识别作弊，提升FPS游戏反外挂效率
Identify As A Human Does: A Pathfinder of Next-Generation Anti-Cheat Framework for First-Person Shooter Games
- 模仿人类专家判断流程，结合多视角特征识别作弊
- 在真实数据集上实现更短封禁时间与更低人工成本
- 能发现传统系统漏掉的高级作弊者，适合游戏安全团队使用
游戏产业快速发展，但在线游戏中作弊问题严重威胁游戏体验，尤其在第一人称射击游戏（FPS）中造成巨大损失。现有反外挂方案存在客户端硬件限制、安全风险、服务器端不可靠方法以及缺乏全面真实数据集等问题。为此，本文提出HAWK——一种针对热门游戏CS:GO的服务器端反作弊框架。HAWK采用机器学习技术模拟人类专家的识别过程，引入创新的多视角特征，并具备清晰的工作流程。作者利用首个包含多种作弊类型和复杂程度的大规模真实数据集对HAWK进行评估，结果显示其具备优异效率与可接受开销，相比现有反作弊系统封禁时间更短，显著减少人工干预，且能捕捉到逃避官方检测的作弊者。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。