一种基于工作流网的游戏作弊检测系统

doi:10.16182/j.issn1004731x.joss.18-0243

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (3): 455-463.doi: 10.16182/j.issn1004731x.joss.18-0243

• 仿真模型/系统置信度评估技术 • 上一篇下一篇

一种基于工作流网的游戏作弊检测系统

郑红¹, 叶成¹, 邓文轩¹, 潘理²

1. 华东理工大学,上海 200237;
2. 湖南理工学院,湖南岳阳 414006

收稿日期:2018-04-27 修回日期:2018-10-18 出版日期:2020-03-18 发布日期:2020-03-25
作者简介:郑红(1973-),女,江苏淮南,博士,副教授,研究方向为形式化建模、服务计算。
基金资助:
国家自然科学基金(61103115,61103172),湖南省自然科学基金(11JM058,11JJ2037),上海市科委科技创新行动计划高新技术领域项目(16511101000)

A Game Cheating Detection System Based on Petri Nets

Zheng Hong¹, Ye Cheng¹, Deng Wenxuan¹, Pan Li²

1. East China University of Science and Technology, Shanghai 200237, China;
2. Hunan Institute of Science and Technology, Yueyang 414006, China

Received:2018-04-27 Revised:2018-10-18 Online:2020-03-18 Published:2020-03-25

摘要/Abstract

摘要： 游戏作弊对网络游戏危害很大,目前反作弊技术的瓶颈是效率低并且过多依赖于签名匹配。提出一种高效且不依赖于签名匹配的游戏作弊检测系统,用工作流网对作弊行为建模,通过流程树对作弊行为模型进行融合,在不影响检测结果的前提下节省存储空间。用热门网游的作弊软件对系统进行实验,存储空间降低率达到约60%,证明了该系统框架是可行、有效的。

关键词: Petri网, 工作流网, 流程树, 游戏作弊检测, 模型融合

Abstract: Game cheating endangers the normal cyber game seriously. The bottlenecks of the anti-cheating technology are the low efficiency and its too much dependence on the signature matching. Not relying on the signature matching, a highly efficient game cheating detection system is proposed, which models by process trees. This can save the storage space without affecting the detection results. The system is tested with the cheating softwares on the popular cyber game and the fusion efficiency of the update model is evaluated. The results show that the storage space reduction rate can reach about 60%, which means effectiveness and feasibility of the system framework.

Key words: petri nets, workflow nets, process trees, game cheating detection, model fusion

中图分类号:

TP391.9

郑红, 叶成, 邓文轩, 潘理. 一种基于工作流网的游戏作弊检测系统[J]. 系统仿真学报, 2020, 32(3): 455-463.

Zheng Hong, Ye Cheng, Deng Wenxuan, Pan Li. A Game Cheating Detection System Based on Petri Nets[J]. Journal of System Simulation, 2020, 32(3): 455-463.

参考文献

[1] 智研咨询集团. 2015-2020年中国网络游戏行业深度调研及市场前景预测报告[R/OL]. 2015.10/2018.08. http:// www.chyxx.com/research/201510/347879.html.
2015-2020 In-depth research and market prospect forecast report of China's online game industry [R/OL]. 2015.10/2018.08. http://www.chyxx.com/research/2015 10/347879.html.
[2] 中国互联网络信息中心. 第40次中国互联网络发展状况统计报告[R/OL]. 2017.07/2018.08. http://www.cac. gov.cn/2017-08/04/c_1121427728.htm.
China Internet Network Information Center. The 40th China Statistical Report on Internet Development[R/OL]. 2017.07/2018.08. http://www.cac.gov.cn/2017-08/04/c_1 121427728.htm.
[3] Jeff Y, Randell B.A systematic classification of cheating in online games[C]. ACM SIGCOMM Workshop on Network and System Support for Games ACM. New York, USA: Association for Computing Machinery, 2005: 1-9.
[4] Pritchard M. How to Hurt the Hackers: The Scoop on Internet Cheating and How You Can Combat It[R/OL].2000.07/2018.08.https://www.gamasutra.com/view/feature/131557/how_to_hurt_the_hackers_the_scoop_.php.
[5] Liu D, Gao X, Zhang M, et al.Detecting Passive Cheats in Online Games via Performance-Skillfulness Inconsistency[C]. IEEE/IFIP International Conference on Dependable Systems and Networks. Colorado, USA: IEEE, 2017: 615-626.
[6] Morningstar C, Farmer F R.The lessons of Lucasfilm's habitat[J]. Cyberspace First Steps(S1941-8477). 2008 (1): 273-302.
[7] Kaiser E, Feng W C, Schluessler T.Fides: remote anomaly-based cheat detection using client emulation[C]. ACM Conference on Computer and Communications Security, CCS 2009, Chicago, Illinois, USA, November. Chicago, USA: DBLP, 2009: 269-279.
[8] Bethea D, Cochran R A, Reiter M K.Server-side verification of client behavior in online games[J]. Acm Transactions on Information & System Security (S1094-9224), 2008, 14(4): 1-27.
[9] Cochran R, Reiter M.Toward online verfication of client behavior in distributed applications[C]. NDSS 2013. San Diego, California: 20th Network & Distributed System Security Symposium, 2013.
[10] Baughman N E, Levine B N.Cheat-proof playout for centralized and distributed online games[C]. INFOCOM 2001. Twentieth Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. Alaska, USA: IEEE, 2001, 1: 104-1131.
[11] Alayed H, Frangoudes F, Neuman C.Behavioral-based cheating detection in online first person shooters using machine learning techniques[C]. Computational Intelligence in Games. Niagara Falls, Canada: IEEE, 2013: 1-8.
[12] Galli L, Loiacono D, Cardamone L, et al.A cheating detection framework for Unreal Tournament III: A machine learning approach[C]. Computational Intelligence and Games. Seoul, South Korea: IEEE, 2011: 266-272.
[13] Yu S Y, Hammerla N, Yan J, et al.Aimbot Detection in Online FPS Games Using a Heuristic Method Based on Distribution Comparison Matrix[C]. International Conference on Neural Information Processing. Doha, Qatar: Springer, Berlin, Heidelberg, 2012: 654-661.
[14] Yu S Y, Hammerla N, Yan J, et al.A statistical aimbot detection method for online FPS games[C]. International Joint Conference on Neural Networks. Brisbane, Australia: IEEE, 2012: 1-8.
[15] Yeung S F, Lui J C.Dynamic Bayesian approach for detecting cheats in multi-player online games[J]. Multimedia Systems(S0942-4962), 2008, 14(4): 221-236.
[16] 孟庆丰, 李海波. 面向过程的资源组合排斥冲突检测方法[J]. 计算机应用与软件, 2016, 33(4): 22-24, 43.
Meng Qingfeng, Li Haibo.Process-oriented Collision Detection Method for Resources Composition Exclusion[J]. Computer Applications and Software, 2016, 33(4): 22-24, 43.
[17] 孙亚男, 杜玉越, 刘伟. 高速公路冰雪应急流程挖掘与模型改进方法[J]. 计算机科学与探索, 2015, 9(12): 1523-1530.
Sun Ya'nan, Du Yuyue, Liu Wei.Highway Snow and Ice Emergency Response Process Mining and Model Improvement[J]. Journal of Frontiers of Computer Science & Technology, 2015, 9(12): 1523-1530.
[18] Labadi K, Chen H.Modelling, analysis, and optimization of supply chains by using Petri net models: The state-of-the-art[J]. International Journal of Business Performance and Supply Chain Modelling (S1758-9401), 2010, 2(3/4): 188-215.
[19] Chen H, Amodeo L, Chu F, et al.Performance Evaluation and Optimization of Supply Chains modelled by Batch Deterministic and Stochastic Petri Nets[J]. IEEE transactions on Automation Science and Engineering (S1545-5955), 2005, 2(2): 132-144.
[20] 孙静, 秦世引, 宋永华, 等. 一种基于Petri网和概率信息的电力系统故障诊断方法[J]. 电力系统自动化, 2003, 27(13): 10-14, 23.
Sun Jing, Qin Shiyin, Song Yonghua, et al.A fault diagnosis method for power system based on Petri net and probability information[J]. Automation of Electric Power Systems, 2003, 27(13): 10-14, 23.
[21] 庞善臣, 蒋昌俊. Petri网在工作流系统建模和分析中的应用[J]. 系统仿真学报, 2005, 17(增1): 100-104.
Pang Shanchen, Jiang Changjun.Petri net and its application in workflow system modeling and analysis[J]. Journal of System Simulation, 2005, 17(S1): 100-104.
[22] 许荣斌, 石军, 张鹏飞, 等. Petri网的映射变迁关系相似性度量[J]. 浙江大学学报, 2017, 51(6): 1205-1213.
Xu Rongbin, Shi Jun, Zhang Pengfei, et al.Similarity measurement of transition mapping relation using Petri net[J]. Journal of Zhejiang University, 2017, 51(6): 1205-1213.
[23] Li J, Zhou M C, Dai X Z.Reduction and refinement by algebraicoperations for Petri net transformation[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans (S1083-4427), 2012, 42(5): 1244-1255.
[24] 杜玉越, 孙亚男, 刘伟. 基于Petri网的模型偏差域识别与模型修正[J]. 计算机研究与发展, 2016, 53(8): 1766-1780.
Du Yuyue, Sun Ya'nan, Liu Wei.Petri Nets Based Recognition of Model Deviation Domains and Model Repair[J]. Journal of Computer Research and Development, 2016, 53(8): 1766-1780.
[25] 方欢, 何路路, 方贤文, 等. 基于搜索树的业务流程Petri网模型抽象化简方法[J]. 控制理论与应用, 2018, 35(1): 92-102.
Fang Huan, He Lulu, Fang Xianwen, at al. A search-tree-based abstraction method for business process Petri nets models[J]. Control Theory and Applications, 2018, 35(1): 92-102.
[26] Li J Q, Fan Y S.Workflow Model Validation Based on Petri Net Simplification Method[J]. Information and Control (S0019-9958), 2001, 6: 492-497.
[27] Tan W, Xu W, Yang F, et al.A framework for service enterprise workflow simulation with multi-agents cooperation[J]. Enterprise Information Systems (S1751-7575), 2013, 7(4): 523-542.
[28] Aalst W V D, Buijs J, Dongen B V. Towards Improving the Representational Bias of Process Mining[J]. Lecture Notes in Business Information Processing(S1865-1348), 2017, 116: 39-54.

一种基于工作流网的游戏作弊检测系统

A Game Cheating Detection System Based on Petri Nets

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	黄光球, 吴甜甜. 基于对象函数Petri网的关联区域VOCs危害成因解析[J]. 系统仿真学报, 2022, 34(3): 470-481.
[2]	黄光球, 何通, 陆秋琴. 矿尘迁移致生态系统损害级联传播评价研究[J]. 系统仿真学报, 2020, 32(2): 278-288.
[3]	张志利, 张云荣, 马世欣. 基于CPN Tools的“多种权限+操作队列”并发冲突控制方法研究[J]. 系统仿真学报, 2019, 31(6): 1092-1100.
[4]	张云荣, 张志利, 李向阳, 徐卫昌, 韩奎侠, 李良. 虚拟维修人员与工步等级匹配调解方法研究[J]. 系统仿真学报, 2019, 31(4): 624-631.
[5]	孙启鹏, 郭鑫, 马飞. 基于petri网的城际旅客出行服务流程建模与仿真[J]. 系统仿真学报, 2019, 31(2): 189-198.
[6]	周鹏飞, 邢小伟. 回字形布置的双轨轮小车自动化码头仿真[J]. 系统仿真学报, 2018, 30(9): 3264-3273.
[7]	苏佳, 黄光球. 矿区生态系统脆弱性的对象函数Petri网评价模型[J]. 系统仿真学报, 2018, 30(5): 1681-1689.
[8]	黄光球, 刘权宸, 陆秋琴. 矿区生态环境脆弱度动态评价的随机Petri网方法[J]. 系统仿真学报, 2018, 30(5): 1787-1795.
[9]	高迪驹, 兰熙, 沈爱弟. 基于Petri网的锂电池故障诊断系统[J]. 系统仿真学报, 2018, 30(2): 614-621.
[10]	黄光球, 陆秋琴. 广义随机系统动力学相关性分析模型[J]. 系统仿真学报, 2018, 30(1): 69-79.
[11]	黄光球, 白璐. 基于对象Petri网的信任攻击建模与分析[J]. 系统仿真学报, 2017, 29(8): 1702-1711.
[12]	李刚, 韩龙美, 朱永利. 基于时空Petri网的复杂软件系统可信建模与分析[J]. 系统仿真学报, 2016, 28(8): 1740-1747.
[13]	何坤金, 王淋, 刘建新, 陈正鸣, 陈小中. 基于视频的静态场景与运动物体三维融合研究[J]. 系统仿真学报, 2016, 28(1): 63-69.
[14]	郝莉莉, 顾浩, 康凤举, 杨惠珍. 不确定条件下AUV编队决策系统的WIFTCPN形式化建模[J]. 系统仿真学报, 2015, 27(8): 1740-1746.
[15]	方欢, 陆阳, 岳峰, 官骏鸣. 实现故障无二义诊断的部分可观系统设计方法[J]. 系统仿真学报, 2015, 27(3): 470-479.