Artificial Intelligence Helps Computer Network Fault Diagnosis and Optimization

Studying and applying artificial intelligence techniques to solve the accuracy problem of computer network error diagnosis and optimize network performance, a core problem. Six algorithms to solve four major problems (physical connection errors, configuration errors, network overload errors, security attack errors) for network error detection and optimization have been investigated and explained. After the experimental verification, an improved support vector model in this study can recognize physical connection errors with just 0.24 seconds and a security attack error of 5.35 seconds. This result not only confirms the effectiveness and accuracy of artificial intelligence techniques in network diagnostics but also provides a new optimization strategy for network operation that contributes to the stability and security of network services.

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Publication Details

Published
2025-06-06
DOI
https://doi.org/10.1109/icicke65317.2025.11136357
Citations
1
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
1.49
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article

Artificial Intelligence Helps Computer Network Fault Diagnosis and Optimization

Yaling Zhang, Xiaojun Li, Jeffrey Cheng, Xiande Zhuo
1 citations
Software System Performance and Reliability
1.49
article

Artificial Intelligence Helps Computer Network Fault Diagnosis and Optimization

Yaling Zhang, Xiaojun Li, Jeffrey Cheng, Xiande Zhuo
article en
1 citations

Abstract

Studying and applying artificial intelligence techniques to solve the accuracy problem of computer network error diagnosis and optimize network performance, a core problem. Six algorithms to solve four major problems (physical connection errors, configuration errors, network overload errors, security attack errors) for network error detection and optimization have been investigated and explained. After the experimental verification, an improved support vector model in this study can recognize physical connection errors with just 0.24 seconds and a security attack error of 5.35 seconds. This result not only confirms the effectiveness and accuracy of artificial intelligence techniques in network diagnostics but also provides a new optimization strategy for network operation that contributes to the stability and security of network services.

Chengdu Medical College (CN), Chengdu University (CN), Inner Mongolia University of Technology (CN)
Openalex Percentile: Top 13%
Software System Performance and Reliability
1.49
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