Author:
Ghanem Mohamed C.,Chen Thomas M.
Abstract
Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The proposed system is named Intelligent Automated Penetration Testing System (IAPTS) consisting of a module that integrates with industrial PT frameworks to enable them to capture information, learn from experience, and reproduce tests in future similar testing cases. IAPTS aims to save human resources while producing much-enhanced results in terms of time consumption, reliability and frequency of testing. IAPTS takes the approach of modeling PT environments and tasks as a partially observed Markov decision process (POMDP) problem which is solved by POMDP-solver. Although the scope of this paper is limited to network infrastructures PT planning and not the entire practice, the obtained results support the hypothesis that RL can enhance PT beyond the capabilities of any human PT expert in terms of time consumed, covered attacking vectors, accuracy and reliability of the outputs. In addition, this work tackles the complex problem of expertise capturing and re-use by allowing the IAPTS learning module to store and re-use PT policies in the same way that a human PT expert would learn but in a more efficient way.
Reference32 articles.
1. A Guide for Running an Effective Penetration Testing Program;Creasey,2017
2. Attack planning in the real world;Obes;arXiv,2013
3. Partially Observable Markov Decision Processes, Reinforcement Learning: State of the Art;Spaan,2012
Cited by
75 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A Red Teaming Framework for Securing AI in Maritime Autonomous Systems;Applied Artificial Intelligence;2024-09-04
2. AI-Powered Penetration Testing using Shennina: From Simulation to Validation;Proceedings of the 19th International Conference on Availability, Reliability and Security;2024-07-30
3. Automating Penetration Testing with MeTeOr;2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW);2024-07-08
4. Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
5. AutoRed: Automating Red Team Assessment via Strategic Thinking Using Reinforcement Learning;Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy;2024-06-19