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

TL;DR

This paper introduces NetSemantic, a novel framework that uses large language models and semanticization techniques to enable rapid, adaptable, and effective network fault diagnosis across diverse scenarios without prior training.

Contribution

The paper proposes the LNSG algorithm for representing network information semantically, enabling a plug-and-play fault diagnosis framework that is data-independent and adaptable to multiple network environments.

Findings

Outperforms existing methods in zero-shot network fault diagnosis.

Achieves rapid adaptation to various network scenarios.

Demonstrates high accuracy in complex network environments.

Abstract

Leverage large language model (LLM) to refer the fault is considered to be a potential solution for intelligent network fault diagnosis. However, how to represent network information in a paradigm that can be understood by LLMs has always been a core issue that has puzzled scholars in the field of network intelligence. To address this issue, we propose LLM-based Network Semantic Generation (LNSG) algorithm, which integrates semanticization and symbolization methods to uniformly describe the entire multi-modal network information. Based on the LNSG and LLMs, we present NetSemantic, a plug-and-play, data-independent, network information semantic fault diagnosis framework. It enables rapid adaptation to various network environments and provides efficient fault diagnosis capabilities. Experimental results demonstrate that NetSemantic excels in network fault diagnosis across various complex scenarios in a zero-shot manner.