A workshop synthesis provides a decomposition framework for RL-cyber environment interfaces and best-practice guidelines for training and evaluating autonomous cyber defence agents.
Autonomous network defence using reinforcement learning
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CyberOps-Bots is a hierarchical LLM-empowered multi-agent RL framework that reports 68.5% higher network availability and 34.7% better jumpstart performance in new scenarios without retraining on real cloud datasets.
citing papers explorer
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Building Better Environments for Autonomous Cyber Defence
A workshop synthesis provides a decomposition framework for RL-cyber environment interfaces and best-practice guidelines for training and evaluating autonomous cyber defence agents.
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Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework
CyberOps-Bots is a hierarchical LLM-empowered multi-agent RL framework that reports 68.5% higher network availability and 34.7% better jumpstart performance in new scenarios without retraining on real cloud datasets.