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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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Barr, M., Copeland-Stewart, A.: Playing video games during the COVID-19 pandemic and effects on players’ well-being. Games Cult. 17, 122–139 (2021). https://doi.org/10.1177/15554120211017036
Chen, K.T., Pao, H.K.K., Chang, H.C.: Game bot identification based on manifold learning. In: Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games, pp. 21–26 (2008)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Ducheneaut, N., Moore, R.J.: The social side of gaming: a study of interaction patterns in a massively multiplayer online game. In: Proceedings of the 2004 ACM Conference on Computer Supported Cooperative Work, pp. 360–369 (2004)
Hilaire, S., Kim, H.c., Kim, C.k.: How to deal with bot scum in MMORPGs? In: 2010 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2010), pp. 1–6. IEEE (2010)
Kang, A.R., Jeong, S.H., Mohaisen, A., Kim, H.K.: Multimodal game bot detection using user behavioral characteristics. SpringerPlus 5(1), 1–19 (2016). https://doi.org/10.1186/s40064-016-2122-8
Karim, F., Majumdar, S., Darabi, H., Harford, S.: Multivariate LSTM-FCNs for time series classification. Neural Netw. 116, 237–245 (2019)
Kwon, H., et al.: Crime scene reconstruction: online gold farming network analysis. IEEE Trans. Inf. Forensics Secur. 12(3), 544–556 (2016)
Lee, E., Woo, J., Kim, H., Mohaisen, A., Kim, H.K.: You are a game bot!: uncovering game bots in MMORPGs via self-similarity in the wild. In: Ndss (2016)
Liu, S., et al.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. Int. J. Mach. Learn. Cybern. 11(12), 2849–2856 (2020). https://doi.org/10.1007/s13042-020-01155-x
Lu, Z., Lv, W., Xie, Z., Du, B., Huang, R.: Leveraging graph neural network with lstm for traffic speed prediction. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 74–81. IEEE (2019)
Oh, J., Borbora, Z.H., Sharma, D., Srivastava, J.: Bot detection based on social interactions in MMORPGs. In: 2013 International Conference on Social Computing, pp. 536–543. IEEE (2013)
Pao, H.K., Chen, K.T., Chang, H.C.: Game bot detection via avatar trajectory analysis. IEEE Trans. Comput. Intell. AI Games 2(3), 162–175 (2010)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)
Sesti, N., Garau-Luis, J.J., Crawley, E., Cameron, B.: Integrating LSTMs and GNNs for COVID-19 forecasting. arXiv preprint arXiv:2108.10052 (2021)
Tao, J., Xu, J., Gong, L., Li, Y., Fan, C., Zhao, Z.: NGUARD: a game bot detection framework for NetEase MMORPGs. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 811–820 (2018)
Thawonmas, R., Kashifuji, Y., Chen, K.T.: Detection of MMORPG bots based on behavior analysis. In: Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology, pp. 91–94 (2008)
Varvello, M., Voelker, G.M.: Second life: a social network of humans and bots. In: Proceedings of the 20th International Workshop on Network and Operating Systems Support for Digital Audio and Video, pp. 9–14 (2010)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)
Xu, J., et al.: NGUARD+ an attention-based game bot detection framework via player behavior sequences. ACM Trans. Knowl. Discov. Data (TKDD) 14(6), 1–24 (2020)
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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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