Abstract
Some players of Massively Multiuser Online Role-Playing Games (MMORPG) manipulate game bots to accumulate property quickly in the game world, for getting a high-level experience quickly without spending too much time and energy. It has a great impact on the game experience of human players, and lead to an unfair phenomenon in games. We analyze and screen players in online games to quickly capture game bots, and let game operators do subsequent processing. First, we analyze game log data and arrange user behavior sequences to form a matrix with user information. Second, Extreme Learning Machine (ELM) is used for classification and screening. Some traditional classification methods, i.e. SVM and KNN, are used on the same data to verify the algorithm effect. Empirical study demonstrates that the proposed method is competitive with some traditional methods in terms of accuracy and efficiency.
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Acknowledgments
This work was partly supported by National Natural Science Foundation of China (61202290, 61370173, 61772198). We are grateful to the anonymous referees for their insightful comments and suggestions, which clarified the presentation.
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Huang, X., Fan, J., Gao, S., Hu, W. (2018). Online Game Bot Detection Based on Extreme Learning Machine. In: Pan, Z., Cheok, A., Müller, W. (eds) Transactions on Edutainment XIV. Lecture Notes in Computer Science(), vol 10790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56689-3_13
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DOI: https://doi.org/10.1007/978-3-662-56689-3_13
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