Automating Privilege Escalation with Deep Reinforcement Learning
This work addresses the problem of data scarcity for cybersecurity training by providing a tool for red teaming, though it is incremental as it applies existing reinforcement learning methods to a specific domain.
The authors tackled the challenge of generating realistic attack data for training machine learning-based defenses by developing an autonomous agent using deep reinforcement learning to perform local privilege escalation in a Windows 7 environment, demonstrating its ability to adapt techniques based on configuration.
AI-based defensive solutions are necessary to defend networks and information assets against intelligent automated attacks. Gathering enough realistic data for training machine learning-based defenses is a significant practical challenge. An intelligent red teaming agent capable of performing realistic attacks can alleviate this problem. However, there is little scientific evidence demonstrating the feasibility of fully automated attacks using machine learning. In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents. We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation. Our results show that the autonomous agent can escalate privileges in a Windows 7 environment using a wide variety of different techniques depending on the environment configuration it encounters. Hence, our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.