| Feature | Human Pentester | Automated Scanner (e.g., Nessus) | Autopentest-DRL | | :--- | :--- | :--- | :--- | | | Yes | No | Yes | | Adapts to network changes | Slowly | Never | In real-time | | False positive rate | Low (but slow) | Very high | Low (via reward shaping) | | Scalability | 1–5 hosts per day | 10,000 hosts per hour | 500+ hosts per hour with reasoning | | Learning from past engagements | Tacit | Static rules | Weights transfer & fine-tuning |
: 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 autopentest-drl
: Users can retrain the DRL agent on custom network topologies to improve its adaptability and efficiency in specific environments. Why Use DRL for Pentesting? | Feature | Human Pentester | Automated Scanner (e
The agent learns basics: scan → detect vulnerable service → execute correct exploit. Rewards are given immediately. The agent learns basics: scan → detect vulnerable