LNSG algorithm bridges the gap between complex network data and large language models, enabling more effective fault diagnosis across multiple scenarios.

• Transforms technical network information into semantic representations that LLMs can process
• Creates a unified description framework for network entities, their relationships, and anomalies
• Enables plug-and-play integration across different network environments
• Significantly improves generalizability of network fault diagnosis

For security teams, this research offers a breakthrough in automated network troubleshooting, reducing diagnosis time and improving accuracy of threat detection through semantic understanding of network patterns.

Adapting Network Information into Semantics for Generalizable and Plug-and-Play Multi-Scenario Network Diagnosis