期刊文献+

平台动态防御信号博弈策略 被引量:1

Platform dynamic defense strategies based on signaling game
下载PDF
导出
摘要 针对网络博弈中攻防双方拥有信息的不对称性,考虑攻击者据此伪装合法用户隐蔽攻击的情况,研究提出了平台动态防御的信号博弈策略。从平台动态防御原理入手,分析了网络攻防博弈关系和信息的不对称性,基于对访问者类型的推断等要素,构建了平台动态目标防御的信号博弈模型,提出了信号博弈的攻防收益量化指标,给出了信号博弈流程及均衡求解方法。通过示例和仿真分析,演示了信号博弈策略的求解过程。仿真结果表明,在攻击者类型先验概率信息已知的前提下,防御方的信号博弈策略及迁移方案,其期望收益与实际收益最终误差不超过2.7%,理论收益计算方法正确有效;较无差别平台迁移策略,信号博弈策略实际收益最终高出96%,表明该策略能够改善防御方对攻击方信息的不对称性,实施针对性的平台动态防御。 For the asymmetry of information owned by both sides in the network game,a novel signal game strategy is proposed for platform dynamic defense considering the case where an attacker disguises a legitimate user to conceal an attack.Starting from the principle of platform dynamic defense,the relationship between network attack and defense game and the asymmetry of information are analyzed,the signal game model of platform dynamic target defense is constructed based on factors such as the type of visitor,the quantitative index of attack and defense benefit of signal game is put forward,and the signal game flow and equilibrium solution method are given.Through examples and simulation analysis,the solving process of signal game strategy is demonstrated.The simulation results show that,on the premise that the prior probability information of the type of attacker is known,the signal game strategy and transfer scheme of the defense side have a final error of no more than 2.7%between the expected revenue and the actual revenue,and the theoretical revenue calculation method is correct and effective.Compared with the undifferentiated platform migration strategy,the actual return of signal game strategy is 96%higher,which indicates that this strategy can improve the asymmetry of information between the defense side and the attack side,and implement targeted platform dynamic defense.
作者 陈彤睿 王刚 马润年 王志屹 冯云 CHEN Tongrui;WANG Gang;MA Runnian;WANG Zhiyi;FENG Yun(College of Information and Navigation,Air force Engineering University,Xi’an 710003,P.R.China;Unit 94195,Dingxi 730500,P.R.China;Unit 93656,Beijing 101100,P.R.China;Unit 95703,Qujing 655601,P.R.China)
出处 《重庆邮电大学学报(自然科学版)》 CSCD 北大核心 2021年第3期482-490,共9页 Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金 国家自然科学基金(61573017)。
关键词 信号博弈 移动目标防御 平台动态防御 系统漏洞 signaling game moving target defense platform dynamic defense system vulnerability
  • 相关文献

参考文献6

二级参考文献30

  • 1国家质量监督检验检疫总局.GB/T20984--2007信息安全技术信息系统的风险评估规范[S].北京:中国标准出版社,2007.
  • 2Jajodia S, Noel S. Topological vulnerability analysis: A powerful new approach for network attack prevention, de- tection, and response [ M ]//Algorithms, Architectures and Information Systems Security. Singapore: World Scientific Publishing Company ,2008:285 - 305.
  • 3Ou Xinming, Boyer W F, McQueen M A. A scalable ap- proach to attack graph generation[ C ]//Proceedings of the 13th ACM Conference on Computer and Communications Security( CCS' 06). New York : ACM ,2006:336 - 345.
  • 4Ou Xinming,Homer J,Zhang Su,et al. MulVAL project at Kansas State University[EB/OL]. (2011- 12-4) [2015- 11 - 28 ]. http ://people. cis. ksu. edu/- xou/mulval/.
  • 5Xie Peng,Li J H,Ou Xinming,et al. Using Bayesian net- works for cyber security analysis [ C ]//Proceedings of 2010 IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). Chicago : IEEE, 2010 : 211 - 220.
  • 6Homer J, Zhang S, Ou X, et al. Aggregating vulnerability mettles in enterprise networks using attack graphs [J]. lournal of Computer Security,2013,21(4) :561-597.
  • 7Poolsappasit N, Dewri R, Ray I. Dynamic security risk management using Bayesian attack graphs [ J ]. IEEE Transactions on Dependable and Secure Computing,2012, 9 ( 1 ) :61 - 7g.
  • 8Mell P, Scarfone K, Romanosky S. Common vulnerability scoring system [J]. IEEE Security & Privacy Magazine, 2006,4(6) :85 - 89.
  • 9姜伟,方滨兴,田志宏,张宏莉.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. 被引量:153
  • 10张少俊,李建华,宋珊珊,李斓,陈秀真.贝叶斯推理在攻击图节点置信度计算中的应用[J].软件学报,2010,21(9):2376-2386. 被引量:29

共引文献100

同被引文献14

引证文献1

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部