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.
Deep reinforcement learning for cyber security
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Combines particle filtering, feature-based aggregation, and rollout to produce scalable network security policies with theoretical guarantees that adapt quickly to model changes.
citing papers explorer
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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.
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Adaptive Network Security Policies via Belief Aggregation and Rollout
Combines particle filtering, feature-based aggregation, and rollout to produce scalable network security policies with theoretical guarantees that adapt quickly to model changes.