Summary of Entity-based Reinforcement Learning For Autonomous Cyber Defence, by Isaac Symes Thompson et al.
Entity-based Reinforcement Learning for Autonomous Cyber Defence
by Isaac Symes Thompson, Alberto Caron, Chris Hicks, Vasilios Mavroudis
First submitted to arxiv on: 23 Oct 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 This paper addresses the challenge of developing autonomous cyber defense agents that can generalize across diverse network topologies and configurations. Traditional deep reinforcement learning approaches struggle with this problem due to fixed-size observation and action spaces, making it difficult to develop agents that perform well in dynamic environments. The authors propose an entity-based reinforcement learning framework that decomposes the observation and action space into a collection of discrete entities, enabling policy parameterizations specialized in compositional generalization. A Transformer-based policy is trained on the Yawning Titan cyber-security simulation environment and tested across various network topologies, demonstrating significant performance improvements over traditional MLP-based policies. The findings highlight the potential for entity-based reinforcement learning to advance autonomous cyber defense by providing more generalizable policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better computer programs that protect computer networks from harm. Right now, these protection agents are only good at working in specific types of networks. But what if they could work well in any type of network? That’s what this research is trying to figure out. The authors are using a new way of teaching the agent called entity-based reinforcement learning. This method breaks down the task into smaller parts, making it easier for the agent to learn. They tested their idea on a special computer simulation and found that it worked much better than the old way. This could lead to more effective protection for our computer networks in the future. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning » Transformer
