Abstract:In order to solve the problem that all attribute node beliefs are influenced dynamically by the observed attack events in attack graph model,based on Bayesian attack graph,a dynamic risk assessment model was presented.The probability attack graph,which describes the cause consequence relationships among the steps in one attack progress,was built by using Bayesian belief networks.The probability of vulnerabilities, which is successfully executed by an attacker,was computed by using index of common vulnerability scoring system,and the static security risk of the property node was assessed by introducing local conditional probability tables.Then,by combining real time attack events being observed by intrusion detection system,the posterior probability was calculated dynamically when the attack occurred by applying Bayesian inference.Finally,the security risk of the target networks was evaluated.Experimental results showed that the model can assess dynamical security risk and deduce attack path, and provide effective guidance for taking security hardening strategy.