Training an Autonomous Pentester with Deep RL

Explore the intersection of deep reinforcement learning and cybersecurity in this 35-minute conference talk from Strange Loop 2021. Delve into the challenges and potential solutions for creating an autonomous penetration testing agent using deep RL techniques. Learn how the Metasploit framework can be leveraged to define action and state spaces, and discover the innovative use of partially observed Markov decision processes to simulate vulnerable networks for rapid agent training. Examine the process of transitioning the trained agent from simulation to real-world scenarios, demonstrating its ability to compromise actual vulnerable hosts. Gain insights from Shane Caldwell, a machine learning engineer with a background in penetration testing, as he discusses the practical applications and implications of this cutting-edge approach to cybersecurity.

"Training an Autonomous Pentester with Deep RL" by Shane Caldwell