Autopentest-drl [verified]

: It uses a two-stage process: first, it gathers data (using tools like Shodan) to build a topology and attack tree (using MulVAL); then, it applies DRL algorithms to find the most efficient attack paths. Key Technical Components

Discrete actions derived from MITRE ATT&CK: autopentest-drl

from gym import spaces self.action_space = spaces.Discrete(512) # 512 common pentest commands self.observation_space = spaces.Dict( "scan_results": spaces.Box(0, 1, shape=(100,)), "current_priv": spaces.Discrete(3), # user, root, service "compromised_hosts": spaces.Box(0, 1, shape=(10,)) ) : It uses a two-stage process: first, it

at the Japan Advanced Institute of Science and Technology (JAIST). It uses Deep Reinforcement Learning (DRL) service "compromised_hosts": spaces.Box(0

: Unlike many purely theoretical models, it can be used to execute attacks on real networks by interfacing with standard security tools like Nmap for reconnaissance and Metasploit for exploitation.