Discover the Hidden Attack Path in Multiple Domain Cyberspace Based on Reinforcement Learning
Lei Zhang, Hongmei Li, Shiming Xia, Yu Pan, Wei Bai, Feng Qin, Wei Li, Qibin Zheng, Shize Guo, Zhisong Pan · Scientific Programming · 2022
There are certain vulnerabilities at the beginning of multiple domain cyberspace configuration. How to discover these potential vulnerabilities has been a hot topic. This paper proposes to find these cyberspace vulnerabilities by discovering the shortest attack path in multiple domain cyberspace. In order to discover more and shorter attack paths, we train an agent as an attacker to discover multiple domain cyberspace attack paths. We formulate the discovering attack paths as a reinforcement learning (RL) problem. With this technique, we added a multiple domain action select module to RL that can pick an executable action in a state. By using the proposed method, we can discover more hidden attack paths and shorter attack paths to analyze the potential vulnerabilities to cyberspace. Finally, we created a simulated cyberspace experimental environment to test our proposed method. The experimental results show that the proposed method can discover more hidden multiple domain attack paths and shorter attack paths than the existing methods.