Aim Low, Shoot High: Evading Aimbot Detectors by Mimicking User Behavior
This addresses cheating detection in online gaming, presenting a novel evasion method that could undermine current anti-cheat systems.
The paper tackled the problem of evading aimbot detection in online games by developing an adaptive aimbot that mimics professional player behavior, showing it can evade state-of-the-art detection systems like VAC and VACnet in evaluations with professional players.
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.