GitHub

AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning

AutoPentest-DRL is an automated penetration testing framework based on Deep Reinforcement Learning (DRL) techniques. AutoPentest-DRL can determine the most appropriate attack path for a given logical ...
IEEE

Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently

Abstract: Reliable and efficient trajectory generation methods are a fundamental need for autonomous dynamical systems. The goal of this article is to provide a comprehensive tutorial of three major ...
GitHub

Metasploit Exploit CVE Coverage

This repository provides a daily, automatically updated JSON dataset detailing which exploits within the Metasploit Framework are associated with specific Common Vulnerabilities and Exposures (CVE) ...
IEEE

Metasploit Based Automated Penetration Testing Using Reinforcement Learning

Abstract: It's clear that in today's digital age, where threats are rapidly evolving in both scale and complexity, traditional manual penetration testing methods are unable to keep up. This study ...