An Improved Monte Carlo Graph Search Algorithm for Optimal Attack Path Analysis

Abstract

The problem of optimal attack path analysis is one of the hotspots in network security. Many methods are available to calculate an optimal attack path, such as Q-learning algorithm, heuristic algorithms, etc. But most of them have shortcomings. Some methods can lead to the problem of path loss, and some methods render the result un-comprehensive. This article proposes an improved Monte Carlo Graph Search algorithm (IMCGS) to calculate optimal attack paths in target network. IMCGS can avoid the problem of path loss and get comprehensive results quickly. IMCGS is divided into two steps: selection and backpropagation, which is used to calculate optimal attack paths. A weight vector containing priority, host connection number, CVSS value is proposed for every host in an attack path. This vector is used to calculate the evaluation value, the total CVSS value and the average CVSS value of a path in the target network. Result for a sample test network is presented to demonstrate the capabilities of the proposed algorithm to generate optimal attack paths in one single run. The results obtained by IMCGS show good performance and are compared with Ant Colony Optimization Algorithm (ACO) and k-zero attack graph.