Game bots are automated programs that assist cheating users and enable them to obtain huge superiority, leading to an imbalance in the game ecosystem and the collapse of user interest. Therefore, game bot detection becomes particularly important and urgent. Among many kinds of online games, massively multiplayer online role playing games (MMORPGs), such as World of Warcraft and AION, provide immersive gaming experience and attract many loyal fans. At the same time, however, game bots in MMORPGs have proliferated in volume and method, evolving with the real-world detection methods and showing strong diversity, leaving MMORPG bot detection efforts extremely difficult.

To deal with the fast-changing nature of game bots, we here proposed a generalized game bot detection framework for MMORPGs termed NGUARD, denoting NetEase Games’ Guard. NGUARD takes charge of automatically differentiating game bots from humans for MMORPGs. In detail, NGUARD exploits a combination of supervised and unsupervised methods. Supervised models are utilized to detect game bots in observed patterns according to the training data. Meanwhile, unsupervised solutions are employed to detect clustered game bots and help discovering new bots. The game bot detection framework NGUARD has been implemented and deployed in multiple MMORPG productions in the NetEase Game portfolio, achieving remarkable performance improvement and acceleration compared to traditional methods. Moreover, the framework reveals outstanding robustness for game bots in mutated patterns and even in completely new patterns on account of the design of the auto-iteration mechanism.

Paper:nguard.pdf

Jianrong Tao*, Jiarong Xu*, Linxia Gong, Yifu Li, Changjie Fan and Zhou Zhao (*: Equal Contribution). NGUARD: A Game Bot Detection Framework for NetEase MMORPGs. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18). [PDF]

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Chen Chen, Xueyuan Li, Ye Yang, Jiarong Xu, Zuwei Liao, Xinggao Liu and Jinshui Chen and Jiangang Lu. Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller based on Neural Network with System Error Set as Input. In Proceedings of the 12th Asian Control Conference (ASCC'19). [PDF]

Ye Yang, Jinhou Han, Chen Chen, Jiarong Xu, Zuwei Liao, Xinggao Liu, Jinshui Chen and Jiangang Lu. A PSO-LP Cooperative Algorithm for Mixed Integer Nonlinear Programming. In Proceedings of the 12th Asian Control Conference (ASCC'19). [PDF]

Jiarong Xu, Chen Chen, Ye Yang and Jiangang Lu. Dynamic Modeling of Wax Hydrogenation Unit Based on LSTM-DNN Deep Learning. In Proceedings of the 29th Chinese Process Control Conference (CPCC'18).

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Conference PC Members: China Conference on Knowledge Graph and Semantic Computing (CCKS 2018)

2019, Best Poster Award, Singapore, AI Summer School

2016, Best graduation thesis of Donghua University

2016, Outstanding Graduates of Shanghai

2015, National Scholarship, China