game bot identification based on manifold learning
In recent years, online gaming has become one of the most popular Internet activities, but cheating activity, such as the use of game bots, has increased as a consequence. Generally, the gaming community disapproves of the use of game bots, as bot users obtain unreasonable rewards without corresponding efforts. However, bots are hard to detect because they are designed to simulate human game playing behavior and they follow game rules exactly. Existing detection approaches either disrupt players' gaming experiences, or they assume game bots are run as standalone clients or assigned a specific goal, such as aim bots in FPS games. In this paper, we propose a manifold learning approach for detecting game bots. It is a general technique that can be applied to any game in which avatars' movement is controlled by the players directly. Through real-life data traces, we show that the trajectories of human players and those of game bots are very different. In addition, although game bots may endeavor to simulate players' decisions, certain human behavior patterns are difficult to mimic because they are AI-hard. Taking Quake 2 as a case study, we evaluate our scheme's performance based on real-life traces. The results show that the scheme can achieve a detection accuracy of 98\% or higher on a trace of 700 seconds.
online games cheating trajectory  k nn svm isomap classification