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A GNN-Enhanced Game Bot Detection Model for MMORPGs

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Advances in Knowledge Discovery and Data Mining (PAKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13281))

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Abstract

Game bots are automated programs that assist cheating players in obtaining huge superiority in Massively Multiplayer Online Role-Playing Games (MMORPGs), which has led to an imbalance in the gaming ecosystem and a collapse of interest among normal players. Game bot detection aims to identify cheating behaviors to ensure fair competition for MMORPGs. Due to the high practical value, there is much research on game bot detection at present. One main existing method is conventional machine learning algorithms, which require extensive feature engineering and get limited performance. The other main existing method is the recurrent neural network, but it fails to capture the complex behavioral patterns of players. To tackle the above problems, we propose a novel graph neural network-enhanced game bot detection model, namely GB-GNN. In the proposed model, we model players’ trajectories as graph-structured data to capture the player’s complex behavioral patterns that are difficult to reveal by traditional sequential methods. Extensive experiments on three real-world datasets show that GB-GNN outperforms the previous methods.

X. Qi—Work was done during an internship at NetEase.

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Notes

  1. 1.

    https://www.mordorintelligence.com/industry-reports/mobile-games-market.

  2. 2.

    https://www.adjust.com.

  3. 3.

    http://game.163.com/.

  4. 4.

    https://en.wikipedia.org/wiki/Comparison_of_high-definition_smartphone_displays.

  5. 5.

    https://leihuo.163.com/en/games.html?g=game-1nsh.

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Correspondence to Runze Wu .

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Qi, X., Pu, J., Zhao, S., Wu, R., Tao, J. (2022). A GNN-Enhanced Game Bot Detection Model for MMORPGs. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13281. Springer, Cham. https://doi.org/10.1007/978-3-031-05936-0_25

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  • DOI: https://doi.org/10.1007/978-3-031-05936-0_25

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