Proactive Defense Model Based on Non-Zero-Sum Signal Game

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Proactive Defense Model Based on Non-Zero-Sum Signal Game

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[1]HUANG Wanwei,YUAN Bo,WANG Sunan,et al.Proactive Defense Model Based on Non-Zero-Sum Signal Game[J].Journal of Zhengzhou University (Engineering Science),2022,43(01):90-96.[doi:10.13705/j.issn.1671-6833.2021.05.010]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 01 Page number: 90-96 Column: Public date: 2022-01-09

Title:: Proactive Defense Model Based on Non-Zero-Sum Signal Game

Author(s):: HUANG Wanwei¹; YUAN Bo¹; WANG Sunan²; ZHANG Xiaohui³; 1.College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China;
2.School of Electronic and Communication Engineering, Shen Zhen Polytechnic, Shenzhen 518005, China; 3.Henan Xin′an Communication Technology Co., Ltd., Zhengzhou 450001, China

Keywords:: non-zero-sum; signal game; discount factor; optimal defense strategy

CLC:: TP309

DOI:: 10.13705/j.issn.1671-6833.2021.05.010

Abstract:: In recent years, the damages of network attacks such as launched by APT has become more and more serious. Although existing studies based on signal game theory could simulate the APT attack and defense process to a certain extent, they ignored the phenomenon of unequal benefits between the two sides in the process of attack and defense and the multi-stage confrontation process, resulting in the lack of universality of the model and method. In this paper, a proactive defense model based on non-zero-sum signal game was proposed. First of all, the attack and defense game tree was built based on the signal game theory and the analysis of network attack and defense multi-stage confrontation process. Secondly, the non-zero-sum method and discount factor were used to build the multi-stage income of model in the process of offensive and defensive based on the situation of unequal income. On this basis, a quantitative method was proposed for network attack and defense characteristics, and the current optimal defense strategy algorithm was obtained based on the Nash equilibrium and refined Bayesian equilibrium existing in the analysis model. Finally, the model and method were verified by simulation experiments. The results showed the feasibility and effectiveness of the proposed model and method.

References:: ［1］付钰，李洪成，吴晓平，等．基于大数据分析的 APT 攻击检测研究综述［J］．通信学报，2015，36 ( 11) : 1－14．
［2］ IQBAL Z，ANWAＲ Z． SCEＲM: A novel framework for automated management of cyber threat response activities［J］． Future generation computer systems， 2020，108: 687－708．
［3］王耀光，陈伟权，吴镇邦，等．基于混合差分演化的网络入侵检测算法［J］．郑州大学学报( 工学版) ， 2017，38( 6) : 29－32，49．
［4］ XU D J，LI Y D，XIAO L，et al．Prospect theoretic study of cloud storage defense against advanced persistent threats ［C］/ /2016 IEEE Global Communications Conference． Piscataway: IEEE，2016: 1－6．
［5］陈福才，扈红超，刘文彦．网络空间主动防御技术［M］．北京: 科学出版社，2018: 308－310．
［6］ DIJK M，JUELS A，OPＲEA A，et al．FlipIt: the game of “stealthy takeover”［J］．Journal of cryptology，2013，26 ( 4) : 655－713．
［7］糜旗，朱杰，徐超，等．基于 APT 网络攻击的技术研究［J］．计算机与现代化，2014( 10) : 92－94，122．
［8］孙文君，苏旸，曹镇．非对称信息条件下 APT 攻防博弈模型［J］．计算机应用，2017，37( 9) : 2557－2562．
［9］ FANG X P，ZHAI L D，JIA Z P，et al．A game model for predicting the attack path of APT［C］/ /2014 IEEE 12th International Conference on Dependable， Autonomic and Secure Computing． Piscataway: IEEE， 2014: 491－495．
［10］姜伟，方滨兴，田志宏，等．基于攻防博弈模型的网络安全测评和最优主动防御［J］．计算机学报， 2009，32( 4) : 817－827．

［11］姜伟，方滨兴，田志宏，等．基于攻防随机博弈模型的防御策略选取研究［J］．计算机研究与发展， 2010，47( 10) : 1714－1723．

［12］杨义先，钮心忻．安全通论: 刷新网络空间安全观［M］．北京: 电子工业出版社，2018．

［13］孙骞，高岭，刘涛，等．基于非零和博弈的多路径组合攻击防御决策方法［J］．西北大学学报( 自然科学版) ，2019，49( 3) : 343－350．

［14］李静轩，朱俊虎，邱菡，等．基于非零和随机博弈的 APT 攻击主动防御策略选取［J］．计算机应用研究， 2020，37( 10) : 3071－3076，3111．

［15］ YANG H P． Method for behavior-prediction of APT attack based on dynamic Bayesian game［C］/ /2016 IEEE International Conference on Cloud Computing and Big Data Analysis ( ICCCBDA ) ． Piscataway: IEEE，2016: 177－182．

［16］张恒巍，余定坤，韩继红，等．基于攻防信号博弈模型的防御策略选取方法［J］．通信学报，2016，37 ( 5) : 51－61．

［17］张恒巍，李涛．基于多阶段攻防信号博弈的最优主动防御［J］．电子学报，2017，45( 2) : 431－439．

［18］张为，苏旸，陈文武．基于信号博弈的主动防御模型［J］．计算机工程与应用，2018，54( 17) : 77－82．

［19］陈永强，付钰，吴晓平．基于非零和攻防博弈模型的主动防御策略选取方法［J］．计算机应用，2013，33 ( 5) : 1347－1349，1352．

［20］ ZHANG H W，WANG J D，YU D K，et al． Active defense strategy selection based on static Bayesian game［C］/ /Third International Conference on Cyberspace Technology ( CCT 2015 ) ． London: IET， 2015: 1－7．

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Last Update: 2022-01-09