AutoPentest-DRL is an open-source automated penetration testing framework that uses Deep Reinforcement Learning (DRL)
The primary objective of AutoPentest-DRL is to automate the cycle of network reconnaissance, vulnerability analysis, attack path optimization, and payload execution. The platform achieves this through a modular pipeline that connects traditional scanning utilities with advanced deep neural networks. autopentest-drl
Training a DQN on large or complex network topologies requires significant computational power, often making it impractical for small teams. attack path optimization
: A tool that fully automates pentesting using DRL. autopentest-drl
As cloud infrastructures grow increasingly complex, autonomous testing frameworks powered by Deep Reinforcement Learning will shift from a cutting-edge luxury to an absolute enterprise necessity.