Finally, in the phase, AutoPentest-DRL produces the optimal attack path as a sequence of node labels. When used in real attack mode , the framework can interface with the Metasploit Framework via its pymetasploit3 library to automatically execute the planned attack steps against the target network, demonstrating how a real-world hacker might proceed.
| Tool | Technology Base | Key Focus | Real Attack Capability | | :--- | :--- | :--- | :--- | | | DQN, MulVAL, Metasploit | Attack path planning, education | ✅ Yes (with MSF) | | CLAP (Coverage-based RL) | Coverage RL | Diverse adversary behaviors | ❌ Primarily simulation | | NoisyNet-A3C | Advanced Actor-Critic | Partially Observable (POMDP) | ❌ Academic model | | GORGO | RL | 5G DoS vulnerability analysis | ✅ Specific to 5G |
By integrating these three powerful open-source tools—Nmap, MulVAL, and Metasploit—and orchestrating them with a DRL agent, AutoPentest-DRL creates an autonomous offensive security loop that closely mirrors the methodology of a human penetration tester.
At its core, AutoPentest-DRL is a research and learning platform that demonstrates how a DRL agent can learn to plan and execute an attack on a target network. It orchestrates a well-defined, multi-step process to plan its attacks:
Training a DQN on large or complex network topologies requires significant computational power, often making it impractical for small teams. autopentest-drl
Enter , an cutting-edge framework bridging the gap between advanced artificial intelligence and practical, autonomous cybersecurity testing. What is AutoPentest-DRL?
AutoPentest-DRL, as a research-oriented tool, has several dependencies. A typical installation on a Ubuntu 18.04 LTS system requires the following components:
AutoPentest-DRL is an automated penetration testing framework that uses Deep Reinforcement Learning (DRL) to plan and execute attack paths on computer networks. It was developed by the Cyber Range Organization and Design (CROND) Japan Advanced Institute of Science and Technology (JAIST) Framework Overview
Using Taiwan’s Cybersecurity Management Act and Penetration Tes Finally, in the phase, AutoPentest-DRL produces the optimal
The process begins with an , where the user either manually describes a logical network (including its vulnerabilities and connections) or points the tool at a real target network. When targeting a real network, AutoPentest-DRL uses the network scanner Nmap to actively scan the environment, discovering live hosts, open ports, and services. This allows the framework to build a dynamic map of the target’s attack surface.
The addresses this challenge by framing penetration testing as a sequential decision-making problem. By utilizing deep neural networks to process high-dimensional environmental data, the model scales efficiently beyond traditional depth-first or breadth-first graph traversal algorithms.
: Connects to physical networks to identify and test live vulnerabilities using automated penetration testing tools . Educational & Research Utility
If you're looking to get it running immediately, follow these steps: At its core, AutoPentest-DRL is a research and
: Domain randomization and fine-tuning on live staging environments.
The framework utilizes a for agent training.
AutoPentest-DRL’s power lies in its systematic, multi-stage architecture. The framework seamlessly integrates several components to ingest network data, generate attack plans, and execute them.