Autopentest-drl — [work]

AutoPentest-DRL refers to a framework designed to automate penetration testing using Deep Reinforcement Learning (DRL)

1. The Sample Efficiency Problem: DRL typically requires millions of episodes to converge to an optimal policy. In cybersecurity, running millions of full-scale penetration tests against real networks is impossible (due to network disruption) and unethical. Training in simulators (e.g., CybORG, NASimEmu) injects a "sim-to-real" gap: an agent that excels against a simulated vulnerability might fail against a real, nuanced service. autopentest-drl

The framework operates in two distinct modes to bridge the gap between theoretical planning and actual execution: Logical Attack Mode AutoPentest-DRL refers to a framework designed to automate

1. Vulnerability Scanner: A machine learning-based scanner that identified potential vulnerabilities in the target system.
2. Exploit Generator: A DRL-powered module that generated exploits to test the identified vulnerabilities.
3. Attack Simulator: A simulation engine that mimicked real-world attacks on the target system.

Benefits

• Finds complex, multi-step bugs that rule-based fuzzers or random testing may miss.
• Adapts to evolving applications by continuous learning.
• Prioritizes high-value tests, saving CI resources.
• Can target multiple objectives (coverage, performance, security) via reward shaping.

The Autopentest-DRL framework works as follows: Benefits