An intelligent method for building attack paths based on Bayesian attack graphs

Abstract

To address the scenario that there is the subjectivity of prior probability in the attack graph after the introduction of Bayesian network in the network attack model and the failure of attack nodes is not considered, an optimization scheme of the Bayesian attack graph and an intelligent construction method of attack path based on this scheme are proposed. The risk value of the target network is calculated to avoid the subjectivity of the prior probability and the devices are abstracted as attack graph nodes, and the atomic attacks are used as causal inference relations to reconstruct the attack graph. The analysis results show that the method has a significant improvement in the speed of attack graph and attack path generation and attack success rate, and it can perform the intelligent construction of attack path when the attack nodes fail.