This research introduces a novel end-to-end network health management framework leveraging Large Language Models to overcome limitations in traditional approaches.

• Transforms heterogeneous network data into semantic representations for unified processing
• Enables multi-scale adaptivity for diverse device information analysis
• Outperforms conventional ML techniques in anomaly and fault detection
• Provides interpretable results through LLM reasoning capabilities

For security teams, this approach offers enhanced threat detection by identifying subtle anomalies across dynamic heterogeneous networks while reducing false positives and providing actionable insights.

Large Language Model(LLM) assisted End-to-End Network Health Management based on Multi-Scale Semanticization