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)~~