Abstract
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%.
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Lee, J., Lim, J., Cho, W., Kim, H.K. (2015). In-Game Action Sequence Analysis for Game BOT Detection on the Big Data Analysis Platform. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, KC. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-13356-0_32
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DOI: https://doi.org/10.1007/978-3-319-13356-0_32
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