Optimal Security Protection Selection Strategy Based on Markov Model Attack Graph

Abstract

Abstract Intrusion intent and path prediction are important for security administrators to gain insight into the possible threat behavior of attackers. Existing research has mainly focused on path prediction in ideal attack scenarios, yet the ideal attack path is not always the real path taken by an intruder. In order to accurately and comprehensively predict the path information of network intrusion, a multi-step attack path prediction method based on absorbing Markov chains is proposed. Firstly, the node state transfer probability normalization algorithm is designed by using the nil posteriority and absorption of state transfer in absorbing Markov chain, and it is proved that the complete attack graph can correspond to absorbing Markov chain, and the economic indexes of protection cost and attack benefit and the index quantification method are constructed, and the optimal security protection policy selection algorithm based on particle swarm algorithm is proposed, and finally the experimental verification of the model in protection Finally, we experimentally verify the feasibility and effectiveness of the model in protection policy decision-making, which can effectively reduce network security risks and provide more security protection guidance for timely response to network attack threats.