Abstract:
Cheating in online games comes with many consequences for both players and companies. Therefore, cheating detection and prevention is an important part of developing a co...Show MoreMetadata
Abstract:
Cheating in online games comes with many consequences for both players and companies. Therefore, cheating detection and prevention is an important part of developing a commercial online game. Several anti-cheating solutions have been developed by gaming companies. However, most of these companies use cheating detection measures that may involve breaches to users' privacy. In our paper, we provide a server-side anti-cheating solution that uses only game logs. Our method is based on defining an honest player's behavior and cheaters' behavior first. After that, using machine learning classifiers to train cheating models, then detect cheaters. We presented our results in different organizations to show different options for developers, and our methods' results gave a very high accuracy in most of the cases. Finally, we provided a detailed analysis of our results with some useful suggestions for online games developers.
Date of Conference: 11-13 August 2013
Date Added to IEEE Xplore: 17 October 2013
ISBN Information:
ISSN Information:
References is not available for this document.
Select All
1.
G. Hoglund and G. McGraw, Exploiting online games: cheating massively distributed systems, 1st ed. Addison-Wesley Professional, 2007
2.
S. Webb and S. Soh, "A survey on network game cheats and P2P solutions," Australian Journal of Intelligent Information Processing Systems, vol. 9, no. 4, pp. 34-43, 2008
3.
Unity3d-game engine. [Online]. Available: http://www. unity3d. com/
4.
S. Yeung and J. C. Lui, "Dynamic Bayesian approach for detecting cheats in multi-player online games," Multimedia Systems, vol. 14, no. 4, pp. 221-236, 2008. [Online]. Available: http://dx. doi. org/10. 1007/s00530-008-0113-5
5.
L. Galli, D. Loiacono, L. Cardamone, and P. Lanzi, "A cheating detection framework for Unreal Tournament III: A machine learning approach," in CIG, 2011, pp. 266-272
6.
C. Thurau, C. Bauckhage, and G. Sagerer, "Combining Self Organizing Maps and Multilayer Perceptrons to Learn Bot-Behavior for a Commercial Computer Game," in Proc. GAME-ON, 2003, pp. 119-123
7.
C. Thurau, C. Bauckhage, and G. Sagerer, "Learning Human-Like Movement Behavior for Computer Games," in Proc. Int. Conf. on the Simulation of Adaptive Behavior. MIT Press, 2004, pp. 315-323
8.
C. Thurau and C. Bauckhage, "Towards manifold learning for gamebot behavior modeling," in Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology, ser. ACE '05. New York, NY, USA: ACM, 2005, pp. 446-449. [Online]. Available: http://doi. acm. org/10. 1145/1178477. 1178577
9.
C. Thurau, T. Paczian, and C. Bauckhage, "Is Bayesian Imitation Learning the Route to Believable Gamebots?" in Proc. GAME-ON North America, 2005, pp. 3-9
10.
K. Chen, H. Pao, and H. Chang, "Game Bot Identification based on Manifold Learning," in Proceedings of ACM NetGames 2008, 2008
11.
USC GamePipe Laboratory. [Online]. Available: http://gamepipe. usc. edu/
12.
Artificial Aiming. [Online]. Available: http://www. artificialaiming. net/
13.
Weka 3: Data Mining Software in Java. [Online]. Available: http://www. cs. waikato. ac. nz/ml/weka/
14.
J. Platt, "Advances in kernel methods," B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds. Cambridge, MA, USA: MIT Press, 1999, ch. Fast training of support vector machines using sequential minimal optimization, pp. 185-208. [Online]. Available: http://dl. acm. org/citation. cfm?id=299094. 299105
15.
M. Rychetsky, Algorithms and Architectures for Machine Learning Based on Regularized Neural Networks and Support Vector Approaches. Germany: Shaker Verlag GmbH, Dec. 2001
16.
R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Proceedings of the 14th international joint conference on Artificial intelligence-Volume 2, ser. IJCAI'95. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1995, pp. 1137-1143. [Online]. Available: http: //dl. acm. org/citation. cfm?id=1643031. 1643047
17.
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn., vol. 46, no. 1-3, pp. 389-422, mar 2002. [Online]. Available: http://dx. doi. org/10. 1023/A:1012487302797