论文标题
瞄准低，射击高：通过模仿用户行为来逃避AIMBOT检测器
Aim Low, Shoot High: Evading Aimbot Detectors by Mimicking User Behavior
论文作者
论文摘要
当前检测在线游戏中作弊的方案通常是基于以下假设：应用作弊行动采取了与正常行为截然不同的动作。例如，业余球员使用了第一人称射击游戏的Aimbot来增加他/她的能力多次。试图逃避检测将需要减少预期效果，以便将优势降低到微不足道。我们认为，不一定是这种情况，并证明了专业玩家如何利用自适应Aimbot，该Aimbot模仿用户行为以逐渐提高性能并避免最新的检测机制。我们通过两个专业的“反击：全球进攻”参与者，两个开源反欺诈系统以及VAC，VACNET和守望先锋的商业建立的组合，并在定量和定性的评估中展示了这一点。
Current schemes to detect cheating in online games often build on the assumption that the applied cheat takes actions that are drastically different from normal behavior. For instance, an Aimbot for a first-person shooter is used by an amateur player to increase his/her capabilities many times over. Attempts to evade detection would require to reduce the intended effect such that the advantage is presumably lowered into insignificance. We argue that this is not necessarily the case and demonstrate how a professional player is able to make use of an adaptive Aimbot that mimics user behavior to gradually increase performance and thus evades state-of-the-art detection mechanisms. We show this in a quantitative and qualitative evaluation with two professional "Counter-Strike: Global Offensive" players, two open-source Anti-Cheat systems, and the commercially established combination of VAC, VACnet, and Overwatch.
