Entity-based Reinforcement Learning Advances Cyber Defence Techniques
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🤖✨ Entity-based Reinforcement Learning Enhances Autonomous Cyber Defence. A new approach to autonomous cyber defence leverages entity-based reinforcement learning to improve the generalisation of defensive agents across diverse network topologies. Traditional deep reinforcement learning struggles with fixed-size observation and action spaces, limiting adaptability in dynamic environments. By decomposing these spaces into discrete entities, the proposed framework allows for compositional generalisation, significantly outperforming standard multi-layer perceptron (MLP) policies in varied network scenarios. Testing on the Yawning Titan cyber-security simulation shows promising results, including zero-shot generalisation to networks of different sizes, indicating a substantial advancement in creating more robust and adaptable cyber defence systems.
