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Current schemes to detect cheating in online games often build on theassumption that the applied cheat takes actions that are drastically differentfrom normal behavior. For instance, an Aimbot for a first-person shooter isused by an amateur player to increase his/her capabilities many times over.Attempts to evade detection would require to reduce the intended effect suchthat the advantage is presumably lowered into insignificance. We argue thatthis is not necessarily the case and demonstrate how a professional player isable to make use of an adaptive Aimbot that mimics user behavior to graduallyincrease performance and thus evades state-of-the-art detection mechanisms. Weshow 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.

Aim Low, Shoot High: Evading Aimbot Detectors by Mimicking User Behavior