Ziqiao Zhou1, Tianyang Zhou1,*, Jinghao Xu2, Junhu Zhu1
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2613-2634, 2024, DOI:10.32604/cmes.2023.028553
Abstract Intelligent penetration testing is of great significance for the improvement of the security of information systems, and the critical issue is the planning of penetration test paths. In view of the difficulty for attackers to obtain complete network information in realistic network scenarios, Reinforcement Learning (RL) is a promising solution to discover the optimal penetration path under incomplete information about the target network. Existing RL-based methods are challenged by the sizeable discrete action space, which leads to difficulties in the convergence. Moreover, most methods still rely on experts’ knowledge. To address these issues, this paper… More >
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