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SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Data Mining and Knowledge Discovery
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Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Towards manifold learning for gamebot behavior modeling
Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
Identifying MMORPG bots: a traffic analysis approach
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
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CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
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AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Detection of MMORPG bots based on behavior analysis
ACE '08 Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology
Identifying MMORPG bots: a traffic analysis approach
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing applications in network intrusion detection systems
Battle of Botcraft: fighting bots in online games with human observational proofs
Proceedings of the 16th ACM conference on Computer and communications security
Fides: remote anomaly-based cheat detection using client emulation
Proceedings of the 16th ACM conference on Computer and communications security
Trajectory based behavior analysis for user verification
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Server-side verification of client behavior in online games
ACM Transactions on Information and System Security (TISSEC)
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Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology
Trajectory analysis for user verification and recognition
Knowledge-Based Systems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Towards providing security for mobile games
Proceedings of the eighth ACM international workshop on Mobility in the evolving internet architecture
Peer-to-peer architectures for massively multiplayer online games: A Survey
ACM Computing Surveys (CSUR)
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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 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.