Defense decision-making method based on incomplete information stochastic game and Q-learning

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 stochasti...

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Bibliographic Details
Published inTongxin Xuebao Vol. 39; pp. 56 - 68
Main Authors Hongqi ZHANG, Junnan YANG, Chuanfu ZHANG
Format Journal Article
LanguageChinese
English
Published Editorial Department of Journal on Communications 25.08.2018
Subjects
Bayesian Nash equilibrium
defense strategy
network attack and defense
Q-learning
stochastic game
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ISSN1000-436X
DOI10.11959/j.issn.1000-436x.2018145

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Summary: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.
ISSN:1000-436X
DOI:10.11959/j.issn.1000-436x.2018145