Sunday 16 March 2025
Network fault diagnosis is a critical component of ensuring the stability and reliability of modern network operations. Traditional approaches, limited by their training on specific performance metrics for predefined scenarios, struggle to generalize across diverse faults and anomalies in varying network environments.
Recently, large language models (LLMs) have demonstrated strong generalization capabilities across various domains. Building on this success, researchers have proposed a plug-and-play intelligent network fault diagnosis framework based on LLMs, dubbed NetSemantic. This innovative approach transforms multimodal network information into unified textual representations, enabling LLMs to perform reasoning and generate efficient fault resolutions and health assessment reports.
One of the key challenges in traditional network fault diagnosis is the need for manual inspections and physical modeling, which can be time-consuming and error-prone. In contrast, NetSemantic leverages the power of LLMs to automatically analyze network data, reducing the risk of human error and increasing the speed of diagnosis.
The framework consists of three main components: a knowledge graph, a symbolic representation method, and a self-adaptive data updating mechanism. The knowledge graph provides a structured repository for storing network information, allowing LLMs to draw upon relevant knowledge during diagnostic processes. The symbolic representation method transforms logically strong network information into symbols, enabling LLMs to perform logical reasoning and generate accurate diagnoses.
The self-adaptive data updating mechanism dynamically incorporates new network information into the knowledge graph, ensuring that the framework remains up-to-date and accurate even in rapidly changing network environments.
Experimental results demonstrate the effectiveness of NetSemantic in diagnosing network faults across various complex scenarios. The framework achieves significantly higher diagnostic accuracy than traditional approaches, with an average improvement of 5% to 10%. Additionally, NetSemantic enhances the interpretability of network data, providing engineers with valuable insights into fault causes and resolutions.
The framework’s adaptability is also evident in its ability to diagnose faults across different network topologies and node scales. In centralized connection structures, NetSemantic precisely identifies data transmission anomalies between nodes, achieving an impressive diagnostic accuracy of 92.3%. For ring topologies, where data is transmitted in a unidirectional loop, the framework’s diagnostic accuracy reaches 91.7%.
The authors’ approach has significant implications for network management and operations. By leveraging the power of LLMs to automate fault diagnosis, NetSemantic can reduce the time and resources required for troubleshooting, while also increasing the accuracy and reliability of diagnoses.
Cite this article: “Intelligent Network Fault Diagnosis with Large Language Models”, The Science Archive, 2025.
Network Fault Diagnosis, Large Language Models, Intelligent Framework, Multimodal Network Information, Knowledge Graph, Symbolic Representation Method, Self-Adaptive Data Updating Mechanism, Diagnostic Accuracy, Network Topologies, Node Scales
