A Hybrid Approach to Vulnerability Assessment Combining Attack Graph and Hidden Markov

The use of attack graphs in the study of network vulnerability assessment is a classical and effective way, which breaks the deficiency of traditional methods that can only do a static assessment based on the threat level of vulnerabilities. In order to assess the overall vulnerability of the network system, this paper studies different vulnerability assessment methods based on attack graphs and proposes the AGH model, which uses CVSS to statically assess the probability of vulnerability exploitation and combines the Hidden Markov Model and Viterbi decoding algorithm to calculate the maximum probability of attacker attack path. Finally, the feasibility of the method is verified by conducting experiments with a real laboratory network topology.