On Statistical Approaches to Online Game Bot Detection
Date Issued
2009
Date
2009
Author(s)
Liao, Yi-Tsai
Abstract
In recent years, online game has become one of the most popular Internet activities, but cheating activity, such as game bots, has increased as a consequence. Generally, the gamer community disagrees with 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 interrupt the players'' gaming experiences, or assume game bots are run as standalone clients or assigned a specific goal, such as aim bots in FPS games. Therefore, we separately propose game bot detection approaches according to two different types of online games.) A trajectory-based approach to detect FPS game bots. It is a general technique that can be applied to any game in which the avatar''s movement is controlled directly by the players. Through real-life data traces, the result shows 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 result shows that the scheme can achieve a detection accuracy to 95% or higher, given a trace of 200 seconds or longer.) A pressure-error-based approach to detect rhythm game bots. It is also a general technique that can be applied to any rhythm game in which the errors increase the dependance on pressure from the game speed and key combinations. Theoretically, the bot can fight back by learning the real player''s behaviors, but without our arguments of formula, the bot is hard to do that. The study case is Dancing Online. Up to now, we have not found any paper about rhythm game bots detection, so we believe that this paper is the first one for rhythm game bots detection.
Subjects
Cheating Detection
Online Games
Security
Supervised Classification
User Behavior
Type
thesis
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