AutoPentest-DRL is an automated penetration testing framework that uses Deep Reinforcement Learning (DRL) to plan and execute attack paths on computer networks. It was developed by the Cyber Range Organization and Design (CROND) Japan Advanced Institute of Science and Technology (JAIST) Framework Overview
: It analyzes a network's topology (using description files) to determine the most efficient multi-stage attack path without actually launching any exploits. It often utilizes autopentest-drl
Step 4: Reward normalization – Use a running mean and std for rewards to avoid oscillation. Future Directions The Major Hurdles: Sample Inefficiency and
Future Directions
Despite progress, AutoPentest-DRL is not ready for autonomous deployment on unknown critical infrastructure. Three showstopper problems persist: autopentest-drl
Cyber Range Training: Enhancing Capture-the-Flag (CTF) exercises by providing an automated, "smart" adversary that students can defend against.