Preventing bots from playing online games
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Identifying MMORPG bots: a traffic analysis approach
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Game traffic analysis: an MMORPG perspective
Computer Networks: The International Journal of Computer and Telecommunications Networking
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
Networked Graphics: Building Networked Games and Virtual Environments
Networked Graphics: Building Networked Games and Virtual Environments
Trajectory based behavior analysis for user verification
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Grammatical inference and games: extended abstract
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Artificial neural network for bot detection system in MMOGs
Proceedings of the 9th Annual Workshop on Network and Systems Support for Games
Game behavior pattern modeling for game bots detection in MMORPG
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Trajectory analysis for user verification and recognition
Knowledge-Based Systems
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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 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 experience, 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 trajectory-based approach to detect 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, 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 95% or higher given a trace of 200 seconds or longer.