Summary of Mind the Gap: Towards Generalizable Autonomous Penetration Testing Via Domain Randomization and Meta-reinforcement Learning, by Shicheng Zhou et al.
Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning
by Shicheng Zhou, Jingju Liu, Yuliang Lu, Jiahai Yang, Yue Zhang, Jie Chen
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 GAP framework is a generalizable autonomous pentesting framework that aims to train efficient policies in realistic environments while improving agents’ generalization ability. To address the training environment dilemma and poor generalization ability, GAP introduces a Real-to-Sim-to-Real pipeline that enables end-to-end policy learning in unknown real environments, constructs realistic simulations, and improves agents’ generalization ability using domain randomization and meta-reinforcement learning (RL). The framework also leverages large language model-powered domain randomization for synthetic environment generation. Experimental results demonstrate GAP’s effectiveness in enabling policy learning in various realistic environments, achieving zero-shot policy transfer in similar environments, and realizing rapid policy adaptation in dissimilar environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GAP is a new way to teach computers how to learn and adapt quickly when testing computer systems for vulnerabilities. Right now, training these computers in simulated environments makes them good at finding problems, but they don’t always work well when faced with real-world scenarios. GAP fixes this by creating realistic simulations and teaching the computers to generalize from one experience to another. This means they can quickly learn new ways of attacking or defending systems without needing lots of data. The results show that GAP is very effective in helping computers learn how to test computer systems for vulnerabilities. |
Keywords
» Artificial intelligence » Generalization » Large language model » Reinforcement learning » Zero shot
