期刊文献+

基于不完全信息随机博弈与Q-learning的防御决策方法 被引量:9

Defense decision-making method based on incomplete information stochastic game and Q-learning
下载PDF
导出
摘要 针对现有随机博弈大多以完全信息假设为前提,且与网络攻防实际不符的问题,将防御者对攻击者收益的不确定性转化为对攻击者类型的不确定性,构建不完全信息随机博弈模型。针对网络状态转移概率难以确定,导致无法确定求解均衡所需参数的问题,将Q-learning引入随机博弈中,使防御者在攻防对抗中通过学习得到的相关参数求解贝叶斯纳什均衡。在此基础上,设计了能够在线学习的防御决策算法。仿真实验验证了所提方法的有效性。 Most of the existing stochastic games are based on the assumption of complete information,which are not consistent with the fact of network attack and defense.Aiming at this problem,the uncertainty of the attacker’s revenue was transformed to the uncertainty of the attacker type,and then a stochastic game model with incomplete information was constructed.The probability of network state transition is difficult to determine,which makes it impossible to determine the parameter needed to solve the equilibrium.Aiming at this problem,the Q-learning was introduced into stochastic game,which allowed defender to get the relevant parameter by learning in network attack and defense and to solve Bayesian Nash equilibrium.Based on the above,a defense decision algorithm that could learn online was designed.The simulation experiment proves the effectiveness of the proposed method.
作者 张红旗 杨峻楠 张传富 ZHANG Hongqi;YANG Junnan;ZHANG Chuanfu(The Third Institute,Information Engineering University,Zhengzhou 450001,China;Henan Province Key Laboratory of Information Security,Zhengzhou 450001,China)
出处 《通信学报》 EI CSCD 北大核心 2018年第8期56-68,共13页 Journal on Communications
基金 国家高技术研究发展计划("863"计划)基金资助项目(No.2014AA7116082 No.2015AA7116040)~~
关键词 网络攻防 随机博弈 Q-LEARNING 贝叶斯纳什均衡 防御决策 network attack and defense stochastic game Q-learning Bayesian Nash equilibrium defense strategy
  • 相关文献

参考文献5

二级参考文献122

  • 1冯萍慧,连一峰,戴英侠,李闻,张颖君.面向网络系统的脆弱性利用成本估算模型[J].计算机学报,2006,29(8):1375-1382. 被引量:28
  • 2方滨兴.解读信息安全创新突破点[OL].[2008-03-21].http://www.cert.org.cn/articles/news/common/2007051823317.shtml,2008. 被引量:1
  • 3Nash J.Equilibrium points in n-person games[J].Proc of the National Academy of Sciences of the United States of America,1950,36(1):48-49. 被引量:1
  • 4Lee W.Toward cost-sensitive modeling for intrusion detection and response[J].Journal of Computer Security,2002,10(1/2):5-22. 被引量:1
  • 5Wang L Y,Noel S,Jajodia S.Minimum-cost network hardening using attack graphs[J].Computer Communications,2006,29(18):3812-3824. 被引量:1
  • 6Syverson P F.A different look at secure distributed computation[C]//Proc of the 1997 IEEE Computer Security Foundations Workshop.Washington:IEEE Computer Society,1997:109-115. 被引量:1
  • 7Burke D.Towards a game theory model of information warfare[D].Montgomery,AL:Air force Institute of Technology,Air University,1999. 被引量:1
  • 8Lye Kong-wei,Wing J.Game strategies in network security.International Journal of Information Security,2005,4(1/2):71-86. 被引量:1
  • 9Liu P,Zang W.Incentive-based modeling and inference of attacker intent,objectives,and strategies[C]//Proc of the 10th ACM Computer and Communications Security Conf (CCS03).New York:ACM,2003:179-189. 被引量:1
  • 10Jiang Wei,Tian Zhihong,Zhang Hongli,et al.A stochastic game theoretic approach to attack prediction and optimal active defense strategy decision[C]//Proc of 2008 IEEE Int Conf on Networking,Sensing and Control.Washington:IEEE Computer Society,2008:648-653. 被引量:1

共引文献128

同被引文献79

引证文献9

二级引证文献42

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