Autopentest-drl

Autopentest-DRL

Autopentest-DRL is an automated testing framework that integrates deep reinforcement learning (DRL) to generate, prioritize, and execute test cases for software systems. It aims to improve test coverage, find complex bugs, and optimize testing efficiency by learning testing strategies from interactions with the application under test (AUT).

Example Pseudocode

import pytest
import gym
from your_drl_model import DRLModel
Traditional penetration testing is a labor-intensive process that relies heavily on human expertise. AutoPentest-DRL transforms this by reformulating the pentesting task as a sequential decision-making problem. autopentest-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. It uses Deep Reinforcement Learning (DRL)
at the Japan Advanced Institute of Science and Technology (JAIST). It uses Deep Reinforcement Learning (DRL) find complex bugs