Summary of Autonomous Network Defence Using Reinforcement Learning, by Myles Foley et al.
Autonomous Network Defence using Reinforcement Learning
by Myles Foley, Chris Hicks, Kate Highnam, Vasilios Mavroudis
First submitted to arxiv on: 26 Sep 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 autonomous agent in this paper is designed to level the playing field in network security by successfully detecting and countering malicious attacks. The agent uses reinforcement learning to defend against advanced persistent threat (APT) red agents that simulate real-world attacks. A novel agent design is developed and tested in a realistic network environment simulation, featuring 13 hosts across three subnets. The agent demonstrates reliable defense capabilities against both APT red agents, one with complete knowledge of the network layout and another that must discover resources through exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created an artificial intelligence (AI) program to help defend computer networks from hackers. They tested this AI in a simulated network environment where attackers were trying to get into the system. The AI was able to successfully stop both types of attackers, one who knew the layout of the network and another that had to figure it out. This could be an important step in making computer networks safer. |
Keywords
* Artificial intelligence * Reinforcement learning
