The article discusses the Raij u framework, an innovative approach that uses reinforcement learning to automate post-exploitation in network systems. The authors argue that understanding the behaviours of attackers after successful exploitation is crucial in assessing the risks of a network system. They implemented two reinforcement learning algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO), to train agents capable of making intelligent actions. These actions include launching attacks of privileges escalation, gathering hashdump, and lateral movement. The approach allows for automation of certain aspects of the penetration testing workflow, improving efficiency and responsiveness to emerging threats and vulnerabilities.
Publication date: 28 Sep 2023
Project Page: Not provided
Paper: https://arxiv.org/pdf/2309.15518
