research-article

NGUARD: A Game Bot Detection Framework for NetEase MMORPGs

Authors:

Zhou ZhaoAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 811 - 820

https://doi.org/10.1145/3219819.3219925

Published: 19 July 2018 Publication History

Abstract

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.

References

[1]

M. A. Ahmad, B. Keegan, J. Srivastava, D. Williams, and N. Contractor. 2009. Mining for Gold Farmers: Automatic Detection of Deviant Players in MMOGs. In 2009 International Conference on Computational Science and Engineering, Vol. 4. 340--345.

Digital Library

[2]

Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, and Eric Xing. 2008. Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks. In Computer Vision -- ECCV 2008, David Forsyth, Philip Torr, and Andrew Zisserman (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 69--82.

Digital Library

[3]

Mario Luca Bernardi, Marta Cimitile, Fabio Martinelli, and Francesco Mercaldo. 2017. A Time Series Classifcation Approach to Game Bot Detection. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 6, 11 pages.

Digital Library

[4]

Kuan-Ta Chen, Jhih-Wei Jiang, Polly Huang, Hao-Hua Chu, Chin-Laung Lei, and Wen-Chin Chen. 2009. Identifying MMORPG Bots: A Trafc Analysis Approach. EURASIP J. Adv. Signal Process 2009, Article 3 (Jan. 2009), 22 pages.

Digital Library

[5]

Kuan-Ta Chen, Hsing-Kuo Kenneth Pao, and Hong-Chung Chang. 2008. Game Bot Identifcation Based on Manifold Learning. In Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games (NetGames '08). ACM, New York, NY, USA, 21--26.

Digital Library

[6]

YeonJun Choi, SungJune Chang, YongJun Kim, HunJoo Lee, WookHo Son, and SeongIl Jin. 2016. Detecting and monitoring game bots based on large-scale user-behavior log data analysis in multiplayer online games. The Journal of Supercomputing 72, 9 (01 Sep 2016), 3572--3587.

Digital Library

[7]

Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural Language Processing (Almost) from Scratch. J. Mach. Learn. Res. 12 (Nov. 2011), 2493--2537.

Digital Library

[8]

Andrew M Dai and Quoc V Le. 2015. Semi-supervised Sequence Learning. In Advances in Neural Information Processing Systems 28, C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett (Eds.). Curran Associates, Inc., 3079--3087.

Digital Library

[9]

Nicolas Ducheneaut and Robert J. Moore. 2004. 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 (CSCW '04). ACM, New York, NY, USA, 360--369.

Digital Library

[10]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A Densitybased Algorithm for Discovering Clusters a Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96). AAAI Press, 226--231.

Digital Library

[11]

Steven Gianvecchio, Zhenyu Wu, Mengjun Xie, and Haining Wang. 2009. Battle of Botcraft: Fighting Bots in Online Games with Human Observational Proofs. In Proceedings of the 16th ACM Conference on Computer and Communications Security (CCS '09). ACM, New York, NY, USA, 256--268.

Digital Library

[12]

Philippe Golle and Nicolas Ducheneaut. 2005. Preventing Bots from Playing Online Games. Comput. Entertain. 3, 3 (July 2005), 3--3.

Digital Library

[13]

S. Hilaire, H. c. Kim, and C. k. Kim. 2a010. How to deal with bot scum in MMORPGs? 1--6.

[14]

Ah Reum Kang, Seong Hoon Jeong, Aziz Mohaisen, and Huy Kang Kim. 2016. Multimodal game bot detection using user behavioral characteristics. SpringerPlus 5, 1 (26 Apr 2016), 523.

[15]

Hyungil Kim, Sungwoo Hong, and Juntae Kim. 2005. Detection of Auto Programs for MMORPGs. In AI 2005: Advances in Artifcial Intelligence, Shichao Zhang and Ray Jarvis (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1281--1284.

Digital Library

[16]

H. Kwon, A. Mohaisen, J. Woo, Y. Kim, E. Lee, and H. K. Kim. 2017. Crime Scene Reconstruction: Online Gold Farming Network Analysis. IEEE Transactions on Information Forensics and Security 12, 3 (March 2017), 544--556.

Digital Library

[17]

Eunjo Lee, Jiyoung Woo, Hyoungshick Kim, Aziz Mohaisen, and Huy Kang Kim. 2016. You are a Game Bot!: Uncovering Game Bots in MMORPGs via Self-similarity in the Wild. In NDSS.

[18]

Jina Lee, Jiyoun Lim, Wonjun Cho, and Huy Kang Kim. 2015. In-Game Action Sequence Analysis for Game BOT Detection on the Big Data Analysis Platform. In Proceedings of the 18th Asia Pacifc Symposium on Intelligent and Evolutionary Systems - Volume 2, Hisashi Handa, Hisao Ishibuchi, Yew-Soon Ong, and KayChen Tan (Eds.). Springer International Publishing, Cham, 403--414.

[19]

Q. Ma, S. Muthukrishnan, and W. Simpson. 2016. App2Vec: Vector modeling of mobile apps and applications. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 599--606.

Digital Library

[20]

Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. 2014. Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14). IEEE Computer Society, Washington, DC, USA, 1717--1724.

Digital Library

[21]

S. J. Pan and Q. Yang. 2010. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (Oct 2010), 1345--1359.

Digital Library

[22]

C. Platzer. 2011. Sequence-based bot detection in massive multiplayer online games. In 2011 8th International Conference on Information, Communications Signal Processing. 1--5.

[23]

Kusno Prasetya and Zheng da Wu. 2010. Artifcial Neural Network for Bot Detection System in MMOGs. In Proceedings of the 9th Annual Workshop on Network and Systems Support for Games (NetGames '10). IEEE Press, Piscataway, NJ, USA, Article 16, 2 pages.

Digital Library

[24]

Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 3104--3112.

Digital Library

[25]

Ruck Thawonmas, Yoshitaka Kashifuji, and Kuan-Ta Chen. 2008. Detection of MMORPG Bots Based on Behavior Analysis. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (ACE '08). ACM, New York, NY, USA, 91--94.

Digital Library

[26]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 6000--6010.

Digital Library

[27]

Jiyoung Woo and Huy Kang Kim. 2012. Survey and Research Direction on Online Game Security. In Proceedings of the Workshop at SIGGRAPH Asia (WASA '12). ACM, New York, NY, USA, 19--25.

Digital Library

[28]

Kyungmoon Woo, Hyukmin Kwon, Hyun chul Kim, Chong kwon Kim, and Huy Kang Kim. 2011. What can free money tell us on the virtual black market. ACM SIGCOMM (2011).

Digital Library

[29]

Roman V. Yampolskiy and Venu Govindaraju. 2008. Embedded Noninteractive Continuous Bot Detection. Comput. Entertain. 5, 4, Article 7 (March 2008), 11 pages.

Digital Library

Cited By

Han DZhu HChen WPan RLiu YZhou JFeng HZhang TWang XZhu MTao JFan CZhang X(2024)GraphFederator: Federated Visual Analysis for Multi-party Graphs2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00027(172-181)Online publication date: 23-Apr-2024
https://doi.org/10.1109/PacificVis60374.2024.00027
Mulakaluri TPuppala RYasmina CMadhavi PBalaji K. S. V(2024)Reviewing Cheating and Detection Techniques in Massively Multiplayer Online Role-Playing Games: A Systematic Analysis2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)10.1109/IConSCEPT61884.2024.10627799(1-6)Online publication date: 4-Jul-2024
https://doi.org/10.1109/IConSCEPT61884.2024.10627799
Niu HZhou YChen JCai Z(2024)Performance evaluation of lightweight network-based bot detection using mouse movementsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108801135(108801)Online publication date: Sep-2024
https://doi.org/10.1016/j.engappai.2024.108801
Show More Cited By

Index Terms

NGUARD: A Game Bot Detection Framework for NetEase MMORPGs
1. Computing methodologies
  1. Machine learning

Recommendations

NGUARD+: An Attention-based Game Bot Detection Framework via Player Behavior Sequences

Game bots are automated programs that assist cheating users, leading to an imbalance in the game ecosystem and the collapse of user interest. Online games provide immersive gaming experience and attract many loyal fans. However, game bots have ...
A time series classification approach to game bot detection
WIMS '17: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics

Online games consumers are strongly grown in the last years attracted by the always higher quality of the games and the more effective gaming infrastructures. The increasing of on line games market is also concurrent to the diffusion of game bots that ...
BEAT: Behavior Evaluation and Anomaly Tracking, Game Bot Detection Framework in RPG Games
ACAI '23: Proceedings of the 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence

In the realm of gaming, game bots represent a serious threat to the game balance, resulting in decreased enjoyment for legitimate players. For developers, the infiltration of bot players can lead to substantial loss of normal players, ultimately ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '18

Sponsor:

KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

Acceptance Rates

KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

28
Total Citations
View Citations
1,293
Total Downloads

Downloads (Last 12 months)63
Downloads (Last 6 weeks)4

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Han DZhu HChen WPan RLiu YZhou JFeng HZhang TWang XZhu MTao JFan CZhang X(2024)GraphFederator: Federated Visual Analysis for Multi-party Graphs2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00027(172-181)Online publication date: 23-Apr-2024
https://doi.org/10.1109/PacificVis60374.2024.00027
Mulakaluri TPuppala RYasmina CMadhavi PBalaji K. S. V(2024)Reviewing Cheating and Detection Techniques in Massively Multiplayer Online Role-Playing Games: A Systematic Analysis2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)10.1109/IConSCEPT61884.2024.10627799(1-6)Online publication date: 4-Jul-2024
https://doi.org/10.1109/IConSCEPT61884.2024.10627799
Niu HZhou YChen JCai Z(2024)Performance evaluation of lightweight network-based bot detection using mouse movementsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108801135(108801)Online publication date: Sep-2024
https://doi.org/10.1016/j.engappai.2024.108801
Cao HLi YLiang Z(2023)BEAT: Behavior Evaluation and Anomaly Tracking, Game Bot Detection Framework in RPG GamesProceedings of the 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence10.1145/3639631.3639683(309-318)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3639631.3639683
Wen XZhao SWang HWu RQu MHu TChen GTao JFan C(2023)Multi-Source Multi-Label Learning for User Profiling in Online GamesIEEE Transactions on Multimedia10.1109/TMM.2022.317168325(4135-4147)Online publication date: 2023
https://doi.org/10.1109/TMM.2022.3171683
Niu HCheng CCai Z(2023)Learning Human Behavior for Bot Detection: A Perspective on Mouse Movement2023 China Automation Congress (CAC)10.1109/CAC59555.2023.10451138(6575-6580)Online publication date: 17-Nov-2023
https://doi.org/10.1109/CAC59555.2023.10451138
Niu HWei ASong YCai Z(2023)Exploring visual representations of computer mouse movements for bot detection using deep learning approachesExpert Systems with Applications10.1016/j.eswa.2023.120225229(120225)Online publication date: Nov-2023
https://doi.org/10.1016/j.eswa.2023.120225
Hu ZDing YWu RLi LZhang RHu YQiu FZhang ZWang KZhao SZhang YJiang JXi YPu JZhang WWang SChen KZhou TChen JSong YLv TFan C(2023)Deep learning applications in games: a survey from a data perspectiveApplied Intelligence10.1007/s10489-023-05094-253:24(31129-31164)Online publication date: 4-Dec-2023
https://doi.org/10.1007/s10489-023-05094-2
Kang YKim H(2022)Quick and easy game bot detection based on action time interval estimationETRI Journal10.4218/etrij.2022-0089Online publication date: 4-Dec-2022
https://doi.org/10.4218/etrij.2022-0089
Tsaur WTseng CChen C(2022)Effective Bots’ Detection for Online Smartphone Game Using Multilayer Perceptron Neural NetworksSecurity and Communication Networks10.1155/2022/94294752022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9429475
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten