A Monte Carlo Graph Search Algorithm with Ant Colony Optimization for Optimal Attack Path Analysis

Abstract

An optimal attack path is essential for an attacker. With an optimal attack path, the attacker can not only successfully carry out attacks, but also save time, energy and money. This article proposes a Monte Carlo Graph Search algorithm with Ant Colony Optimization(ACO-MCGS) to calculate optimal attack paths in target network. ACO-MCGS can get comprehensive results quickly and avoid the problem of path loss. ACO-MCGS has two steps to calculate optimal attack paths: selection and backpropagation. A weight vector containing host priority, CVSS risk value , host link number is proposed for every host in the target network. The weight vector is applied to improved Ant Colony Optimization algorithm to calculate the evaluation value of every attack path, which is used to screen the optimal attack paths for the first round. The weight vector is also used to calculate the total CVSS value and the average CVSS value of every attack path. Results of our experiment demonstrate the capabilities of the proposed algorithm to generate optimal attack paths in one single run. The results obtained by ACO-MCGS show good performance and are compared with Ant Colony Optimization Algorithm (ACO).