Summary of Knowledge-informed Auto-penetration Testing Based on Reinforcement Learning with Reward Machine, by Yuanliang Li and Hanzheng Dai and Jun Yan
Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine
by Yuanliang Li, Hanzheng Dai, Jun Yan
First submitted to arxiv on: 24 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DRLRM-PT framework utilizes reinforcement learning (RL) and reward machines (RMs) to improve the efficiency of vulnerability identification in information systems through automated penetration testing. By leveraging domain knowledge as guidelines for training a penetration testing policy, DRLRM-PT addresses challenges including poor sampling efficiency, intricate reward specification, and limited interpretability. The study focuses on lateral movement as a penetration testing case study, formulating it as a partially observable Markov decision process (POMDP) guided by RMs. The deep Q-learning algorithm with RM (DQRM) is employed to solve the POMDP and optimize the penetration testing policy. Experimental results demonstrate that DQRM agents exhibit higher training efficiency in penetration testing compared to agents without knowledge embedding, while RMs encoding more detailed domain knowledge demonstrated better penetration testing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated penetration testing uses artificial intelligence to help find weaknesses in computer systems. This approach has been shown to be effective, but there are some challenges it faces. To address these issues, researchers have developed a new framework called DRLRM-PT. This framework combines two ideas: reinforcement learning and reward machines. Reinforcement learning is a way for computers to learn by trial and error. Reward machines provide guidelines that help the computer learn what is important. In this study, the team used DRLRM-PT to improve the efficiency of vulnerability identification in information systems. |
Keywords
» Artificial intelligence » Embedding » Reinforcement learning
