In-Game Action Sequence Analysis for Game BOT Detection on the Big Data Analysis Platform

Nowadays a game BOT becomes a major threat in the online game industry. Game BOTs make the users get more experience points and become a higher-level easily. Therefore, normal users feel unfair and finally leave the game. Accordingly, there have been many efforts to distinguish game BOTs from normal users. Among them, action sequence analysis is effective way compared to other BOT detection methods. However, previous works could not use large datasets because of the limitation of computing power and accessibility to handle large dataset so far. In this paper, we analyzed the full action sequence of users on the big data analysis platform. We evaluated the BOT detection accuracy with real game service data, Blade and Soul. As a result, we showed that full sequence analysis gives the power to detect BOTs precisely. The values for precision and recall were 100%.

[1]  Shichao Zhang,et al.  AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 5-9, 2005, Proceedings , 2005, Australian Conference on Artificial Intelligence.

[2]  Chin-Laung Lei,et al.  Identifying MMORPG Bots: A Traffic Analysis Approach , 2009, EURASIP J. Adv. Signal Process..

[3]  Jaideep Srivastava,et al.  Mining for Gold Farmers: Automatic Detection of Deviant Players in MMOGs , 2009, 2009 International Conference on Computational Science and Engineering.

[4]  Chia-Ming Chang,et al.  A simple scoring algorithm predicting vascular infections in adults with nontyphoid Salmonella bacteremia. , 2012, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[5]  Huy Kang Kim,et al.  Survey and research direction on online game security , 2012, WASA '12.

[6]  Juyong Park,et al.  Online game bot detection based on party-play log analysis , 2013, Comput. Math. Appl..

[7]  Ah Reum Kang,et al.  Chatting Pattern Based Game BOT Detection: Do They Talk Like Us? , 2012 .

[8]  Tadeusz Morzy,et al.  Scalable Hierarchical Clustering Method for Sequences of Categorical Values , 2001, PAKDD.

[9]  Christian Platzer.  Sequence-based bot detection in massive multiplayer online games , 2011, 2011 8th International Conference on Information, Communications & Signal Processing.

[10]  Engin Kirda,et al.  Server-Side Bot Detection in Massively Multiplayer Online Games , 2009, IEEE Security & Privacy.

[11]  Georgios Paliouras,et al.  An evaluation of Naive Bayesian anti-spam filtering , 2000, ArXiv.

[12]  Christian Bauckhage,et al.  Combining Self Organizing Maps and Multilayer Perceptrons to Learn Bot-Behaviour for a Commercial Game , 2003, GAME-ON.

[13]  Christian Bauckhage,et al.  Combining Self Organizing Maps and Multilayer Perceptrons to Learn Bot-Behavior for a Commercial Computer Game , 2003 .

[14]  Huy Kang Kim,et al.  An automatic and proactive identity theft detection model in MMORPGs , 2012 .

[15]  Sungwoo Hong,et al.  Detection of Auto Programs for MMORPGs , 2005, Australian Conference on Artificial Intelligence.

[16]  Hsing-Kuo Kenneth Pao,et al.  Game bot identification based on manifold learning , 2008, NETGAMES.

[17]  Sushil Jajodia,et al.  Blog or block: Detecting blog bots through behavioral biometrics , 2013, Comput. Networks.

[18]  Susan T. Dumais,et al.  A Bayesian Approach to Filtering Junk E-Mail , 1998, AAAI 1998.

[19]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[20]  Paola Sebastiani,et al.  Robust Bayes classifiers , 2001, Artif. Intell..

[21]  Ruck Thawonmas,et al.  Detection of MMORPG bots based on behavior analysis , 2008, ACE '08.

[22]  Huy Kang Kim,et al.  I know what the BOTs did yesterday: Full action sequence analysis using Naïve Bayesian algorithm , 2013, 2013 12th Annual Workshop on Network and Systems Support for Games (NetGames).

[23]  Osmar R. Zaïane,et al.  Clustering Web sessions by sequence alignment , 2002, Proceedings. 13th International Workshop on Database and Expert Systems Applications.

[24]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[25]  Hagit Shatkay,et al.  Approximate queries and representations for large data sequences , 1996, Proceedings of the Twelfth International Conference on Data Engineering.