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.
Developing opti- mal causal cyber-defence agents via cyber security simulation
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A systematic review of neuro-symbolic AI in cybersecurity finds that deeper integration and causal reasoning improve performance across intrusion detection and vulnerability tasks, while identifying barriers and a research roadmap.
A multi-layer framework combining POMDP-level strategic analysis and policy-level Q-value/PER tracking to explain RL-based cyber attacker behavior in simulated environments.
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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Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities
A systematic review of neuro-symbolic AI in cybersecurity finds that deeper integration and causal reasoning improve performance across intrusion detection and vulnerability tasks, while identifying barriers and a research roadmap.
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Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents
A multi-layer framework combining POMDP-level strategic analysis and policy-level Q-value/PER tracking to explain RL-based cyber attacker behavior in simulated environments.