A Unified Modeling Framework for Automated Penetration Testing
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 95 references that are in the map. First posted on arXiv 17 Feb 2025.
Builds on
17 of 95 references.
References · 17
- 2024AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attackscited by 96
- 2019Autonomous Penetration Testing using Reinforcement Learningcited by 33
- 2020Finding Effective Security Strategies through Reinforcement Learning and Self-Playcited by 32
- 2013POMDPs Make Better Hackers: Accounting for Uncertainty in Penetration Testingcited by 14
- 2013Penetration Testing == POMDP Solving?cited by 9
- 2013Attack Planning in the Real Worldcited by 8
- 2021Simulating SQL Injection Vulnerability Exploitation Using Q-Learning Reinforcement Learning Agentscited by 6
- 2024BreachSeek: A Multi-Agent Automated Penetration Testercited by 6
- 2020HARMer: Cyber-attacks Automation and Evaluationcited by 3
- 2020Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learningcited by 3
- 2024Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machinecited by 3
- 2024Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQNcited by 3
5 more
- 2013An Algorithm to Find Optimal Attack Paths in Nondeterministic Scenarioscited by 2
- 2013Automated Attack Planningcited by 1
- 2020Why Charles Can Pen-test: an Evolutionary Approach to Vulnerability Testingcited by 1
- 2021Towards Automated Attack Simulations of BPMN-based Processescited by 1
- 2022'PeriHack': Designing a Serious Game for Cybersecurity Awarenesscited by 1
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Citing papers per year: 2025: 1.
1 paper.