A Unified Modeling Framework for Automated Penetration Testing

TL;DR

This paper introduces AutoPT-Sim, a comprehensive framework for automated penetration testing simulation that unifies various modeling techniques, supports dynamic environments, and provides public datasets and tools for research advancement.

Contribution

It presents AutoPT-Sim, a novel unified modeling framework for multi-dimensional, multi-level, and dynamic network simulation in automated penetration testing.

Findings

AutoPT-Sim supports diverse network scales and dynamic environments.

Public datasets and tools facilitate research and customization.

The framework enhances simulation realism and flexibility.

Abstract

The integration of artificial intelligence into automated penetration testing (AutoPT) has highlighted the necessity of simulation modeling for the training of intelligent agents, due to its cost-efficiency and swift feedback capabilities. Despite the proliferation of AutoPT research, there is a recognized gap in the availability of a unified framework for simulation modeling methods. This paper presents a systematic review and synthesis of existing techniques, introducing MDCPM to categorize studies based on literature objectives, network simulation complexity, dependency of technical and tactical operations, and scenario feedback and variation. To bridge the gap in unified method for multi-dimensional and multi-level simulation modeling, dynamic environment modeling, and the scarcity of public datasets, we introduce AutoPT-Sim, a novel modeling framework that based on policy automation and encompasses the combination of all sub dimensions. AutoPT-Sim offers a comprehensive approach to modeling network environments, attackers, and defenders, transcending the constraints of static modeling and accommodating networks of diverse scales. We publicly release a generated standard network environment dataset and the code of Network Generator. By integrating publicly available datasets flexibly, support is offered for various simulation modeling levels focused on policy automation in MDCPM and the network generator help researchers output customized target network data by adjusting parameters or fine-tuning the network generator.