Autopentest-drl -

AutoPentest-DRL is an open-source framework designed to automate the complex process of penetration testing by leveraging Deep Reinforcement Learning (DRL). Developed by researchers at the Japan Advanced Institute of Science and Technology (JAIST), it aims to simulate human-like decision-making to identify optimal attack paths within a network. Core Architecture and Components

1. Understanding DRL and Testing Needs

• DRL Basics: Deep Reinforcement Learning combines reinforcement learning with deep learning. Agents learn to make decisions by taking actions in an environment to maximize a reward.
• Testing Needs: Unlike traditional software testing, DRL testing is more about ensuring the agent behaves as expected in a wide range of scenarios. This includes testing for performance, safety, and reliability.

| Metric | Rule-based (Metasploit Pro) | AutoPentest-DRL (PPO) | |--------|----------------------------|------------------------| | Time to domain admin | 28 min (median) | 9 min | | Exploit success rate (novel CVEs) | 12% | 67% | | Detection avoidance | Static schedule | Adaptive (learned) | | Actions to root (avg) | 142 | 53 | autopentest-drl

The Benefits of Autopentest-DRL

to make decisions based on the current state of the network. Installation & Setup The project is primarily developed for Ubuntu 18.04 LTS and requires a Python environment. : Source code is available on the AutoPentest-DRL GitHub repository Requirements requirements.txt file to install necessary Python packages. Infrastructure : A pre-configured Docker image whichard/autopentest-drl ) is also available to simplify environment setup. Limitations and Research Context | Metric | Rule-based (Metasploit Pro) | AutoPentest-DRL