Autopentest-drl -

DRL requires millions of iterations to learn optimal strategies. Training directly on real networks is impossibly slow and dangerous. Therefore, frameworks must rely heavily on hyper-accurate network simulations, which are incredibly difficult to build and maintain.

: Analyzes a network topology to determine the optimal attack path without performing actual exploits. This is primarily used for educational and research purposes. Real Attack Mode autopentest-drl

The core of the framework, which uses a Deep Q-Network (DQN) to navigate complex network topologies. It takes a matrix representation of an attack tree as input and outputs the most viable attack path. MulVAL Attack Graph Generator: DRL requires millions of iterations to learn optimal