AutoPentest-DRL is an open-source framework that uses Deep Reinforcement Learning (DRL) to automate cybersecurity penetration testing. Developed by researchers at the Japan Advanced Institute of Science and Technology (JAIST), it is primarily designed as an educational tool to help users study attack mechanisms and identify optimal attack paths in network topologies. 🔍 Core Functionality
Logical & Real Attack Modes: Switch between simulating attack paths on logical topologies or executing real exploits using tools like Nmap and Metasploit.
, a logic-based security analyzer, to generate an attack graph for comparison. Real Attack Mode
Automated Scanning & Exploitation: The framework integrates Nmap for initial vulnerability scanning and Metasploit to execute the suggested exploits automatically .
to determine and execute optimal attack paths against a target network.