Browse > Article
http://dx.doi.org/10.13089/JKIISC.2015.25.5.1131

Game-bot detection based on Clustering of asset-varied location coordinates  

Song, Hyun Min (Graduate School of Information Security, Korea University)
Kim, Huy Kang (Graduate School of Information Security, Korea University)
Abstract
In this paper, we proposed a new approach of machine learning based method for detecting game-bots from normal players in MMORPG by inspecting the player's action log data especially in-game money increasing/decreasing event log data. DBSCAN (Density Based Spatial Clustering of Applications with Noise), an one of density based clustering algorithms, is used to extract the attributes of spatial characteristics of each players such as a number of clusters, a ratio of core points, member points and noise points. Most of all, even game-bot developers know principles of this detection system, they cannot avoid the system because moving a wide area to hunt the monster is very inefficient and unproductive. As the result, game-bots show definite differences from normal players in spatial characteristics such as very low ratio, less than 5%, of noise points while normal player's ratio of noise points is high. In experiments on real action log data of MMORPG, our game-bot detection system shows a good performance with high game-bot detection accuracy.
Keywords
game-bot detection; coordinates clustering; machine learning; classification; online game security;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 M. Nishino, Y. Nakamura, T. Yagi and S. Muto, "A location predictor based on dependencies between multiple lifelog data," Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, ACM, pp. 11-17, Nov. 2010.
2 A. Sridharan and J. Bolot, "Location patterns of mobile users: A large-scale study," in Proc. IEEE INFOCOM13, pp. 1007-1015, Apr. 2013.
3 T. T. Bilgin and A. Y. Camurcu. "A data mining application on air temperature database," Advances in Information Systems. Springer Berlin Heidelberg, pp. 68-76, Oct. 2005.
4 W. He and Z. Liu, "Motion pattern analysis in crowded scenes by using density based clustering," Fuzzy Systems and Knowledge Discovery (FSKD), 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012), pp. 1855-1858, May. 2012.
5 M. Celik, F. Dadaser-Celik, and A. S. Dokuz. "Anomaly detection in temperature data using dbscan algorithm," Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on. IEEE, pp. 91-95, June. 2011.
6 T. M. Thang and J. Kim. "The anomaly detection by using dbscan clustering with multiple parameters," Information Science and Applications (ICISA), 2011 International Conference on. IEEE, pp. 1-5, Apr. 2011.
7 R. Thawonmas, Y. Kashifuji, and K.T. Chen, "Detection of MMORPG bots based on behavior analysis," International Conference on Advances in Computer Entertainment Technology, pp. 91-94, Dec. 2008
8 K.T. Chen and L.W. Hong, "User Identification based on Game-Play Activity Patterns," ACM SIGCOMM workshop on Network and system support for games, pp. 7-12, Set. 2007
9 M. van Kesteren, J. Langevoort, and F. Grootjen, "A step in the right direction: Botdetection in MMORPGs using movement analysis," Proc. of the 21st Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2009), pp. 129-136, Oct. 2009.
10 S. Mitterhofer, C. Platzer, C. Kruegel and E. Kirda, "Server-side bot detection in massive multiplayer online games," IEEE Security and Privacy, pp.29-36, 2009
11 H.K. Pao, K.T. Chen, and H.C. Chang. "Game bot detection via avatar trajectory analysis," Computational Intelligence and AI in Games, IEEE Transactions on, vol. 2 no. 3, pp. 162-175, Sep. 2010   DOI
12 K.T. Chen, H.K. Pao, and H.C. Chang. "Game bot identification based on manifold learning," Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games, ACM, pp. 21-26, Oct. 2008.
13 B. Keegan, M.A. Ahmed and D. Williams, "Sic Transit Gloria Mundi Virtuali? Promise and Peril in the Computational Social Science of Clandestine Organizing," WebSci Conference, pp. 1-8, June. 2011
14 K. Woo, H. Kwon, H.C. Kim, C.K. Kim and H.K. Kim, "What can free money tell us on the virtual black market," ACM SIGCOMM, vol. 41, no. 5, pp. 392-393, Aug. 2011   DOI
15 A.R. Kang, H.K. Kim and J. Woo, "Chatting pattern based game BOT detection: do they talk like us?," KSII Transactions on Internet and Information Systems, vol. 6 no. 11, pp. 2866-2879, Jun. 2013.   DOI
16 A.R. Kang, J. Woo, J. Park and H.K. Kim, "Online game bot detection based on party-play log analysis," Computers & Mathematics with Applications, vol. 65, no. 9, pp.1384-1395, May. 2013.   DOI   ScienceOn
17 J. Lee, J. Lim, W. Cho and H.K. Kim, "In-Game Action Sequence Analysis for Game BOT Detection on the Big Data Analysis Platform," 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp. 403-414, Jan. 2015.
18 H.M. Kwon and H.K. Kim, "Self-similarity based Bot Detection System in MMORPG," Proceedings of the 3th International Conference on Internet. pp. 477-481, May, 2011.
19 M. Ester, H.P. Kriegel, J. Sander and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," Kdd, vol. 96, no. 34, pp. 226-331, Aug. 1996.
20 H.P. Kriegel, P. Kroger and J. Sander, "Density-based clustering," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 3, pp. 231-240, Apr. 2011   DOI
21 D.N. Seo and H.K. Kim, "Detecting Gold-farmers' Groups in MMORPG by connection information," Proceedings of the 3th International Conference on Internet 2011, pp. 583-588, Dec. 2011.
22 J.Y. Woo, A.R. Kang, and H.K. Kim, "The contagion of malicious behaviors in online games," ACM SIGCOMM Computer Communication Review, vol. 43, no. 4, pp. 543-544. Aug. 2013   DOI
23 J.Y. Woo, A.R. Kang, and H.K. Kim, "Modeling of bot usage diffusion across social networks in MMORPGs," Proceedings of the Workshop at SIGGRAPH Asia. ACM, pp. 13-18, Nov. 2012.