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October 3, 2025Open Access

Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agents

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The generalizable approach effectively enables agents to adapt to new network environments and defend against adversaries.
Agents outperform traditional models by reasoning through relational inductive bias and understanding environmental interactions.
This work demonstrates a robust method that addresses overfitting in cybersecurity through graph-based representations.
New strategies in automated cyber defense can significantly bolster protection against various types of cyber threats.

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This framework improves automated cyber defense by enhancing agents’ adaptability across network topologies, suggesting new defense strategies.

King et al. (2025) studied this question.

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This framework improves automated cyber defense by enhancing agents’ adaptability across network topologies, suggesting new defense strategies.