Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework

This paper presents CyberOps-Bots, a system that combines Large Language Models (LLMs, which are AI models trained on text) with reinforcement learning (RL, a type of AI that learns by trial and error) to defend cloud networks against attacks. The system uses a two-layer approach where an upper LLM agent handles planning and human input, while lower RL agents execute specific defense actions, and testing shows it maintains network availability much better than existing methods without needing to retrain when network conditions change.