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AutoPentest-DRL: The Convergence of Deep Reinforcement Learning and Autonomous Penetration Testing

Automated Penetration Testing Using Deep Reinforcement Learning (PDF)

: This paper details how the framework utilizes Deep Q-Learning (DQN) to automate the penetration testing process. It specifically addresses the challenges of scalability and the high dimensionality of action spaces in network security.

• Success rate (reaching target host/privilege)
• Average steps to success
• Unique attack paths discovered

Step 2: Define action and observation spaces

Logical Attack Mode

: This is the simplest mode, intended for educational purposes. It determines the optimal attack path for a simulated network topology without performing actual exploits, allowing users to study attack mechanisms safely. autopentest-drl

The Technical Framework: MDPs and Reward Shaping

Algorithm 1:

AutoPenTest-DRL Training Loop