Abstract
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.
This work was supported in part by Taiwan Information Security Center (TWISC), National Science Council under the grants NSC 97-2219-E-001-001 and NSC 97-2219-E-011-006. It was also supported in part by Taiwan E-Learning & Digital Archives Program (TELDAP), National Science Council under the grants NSC 96-3113-H-001-010 and NSC 96-3113-H-001-012.
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References
Golle, P., Ducheneaut, N.: Preventing bots from playing online games. Computers in Entertainment 3(3), 3 (2005)
von Ahn, L., Blum, M., Hopper, N.J., Langford, J.: CAPTCHA: Using hard AI problems for security. In: Proceedings of Eurocrypt, pp. 294–311 (2003)
Novak, T.P., Hoffman, D.L., Duhachek, A.: The influence of goal-directed and experiential activities on online flow experiences. Journal of Consumer Psychology 13(1), 3–16 (2003)
Ila, S., Mizerski, D., Lam, D.: Comparing the effect of habit in the online game play of australian and indonesian gamers. In: Proceedings of the Australia and New Zealand Marketing Association Conference (2003)
Chen, K.T., Jiang, J.W., Huang, P., Chu, H.H., Lei, C.L., Chen, W.C.: Identifying MMORPG bots: A traffic analysis approach. In: Proceedings of ACM SIGCHI ACE 2006, Los Angeles, USA (June 2006)
Chen, K.T., Huang, P., Lei, C.L.: Game traffic analysis: An MMORPG perspective. Computer Networks 50(16), 3002–3023 (2006)
Yeung, S., Lui, J., Liu, J., Yan, J.: Detecting cheaters for multiplayer games: theory, design and implementation. Proc IEEE CCNC 6, 1178–1182
Malakhov, M.: CR Bot 1.15 (May 2000), http://arton.cunst.net/quake/crbot/
Feltrin, R.R.: Eraser Bot 1.01 (May 2000), http://downloads.gamezone.com/demos/d9862.htm
jibe: ICE Bot 1.0 (1998), http://ice.planetquake.gamespy.com/
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© 2008 IFIP International Federation for Information Processing
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Chen, KT., Liao, A., Pao, HK.K., Chu, HH. (2008). Game Bot Detection Based on Avatar Trajectory. In: Stevens, S.M., Saldamarco, S.J. (eds) Entertainment Computing - ICEC 2008. ICEC 2008. Lecture Notes in Computer Science, vol 5309. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89222-9_11
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DOI: https://doi.org/10.1007/978-3-540-89222-9_11
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