Automating post-exploitation with deep reinforcement learning. Issue 100 (January 2021) - Maeda, Ryusei; Mimura, Mamoru
Abstract: In order to assess the risk of information systems, it is important to investigate the behavior of the attacker after successful exploitation (post-exploitation). However, the audit requires the experts, and to the best of our knowledge, there are no solutions to automate this process. This paper proposes a method of automating post-exploitation by combining deep reinforcement learning and the PowerShell Empire, which is famous as a post-exploitation framework. Our reinforcement learning agents select one of the PowerShell Empire modules as an action. The state of the agents is defined by 10 parameters such as type of account that was compromised by the agents. In the learning phase, we compared the learning progress of the 3 reinforcement learning models: A2C, Q-Learning, and SARSA. The result shows that the A2C could gain reward most efficiently. Moreover, the behavior of the trained agents are evaluated in a test domain network. The results show that the trained agent using A2C could obtain the administrative privileges to the domain controller.