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Application Of Computer Network Fault Diagnosis Based On Neural Network Model

Author

Listed:
  • Wenda Zhou

    (Department of Physics, Cornell University, Ithaca 14853, the United States of America)

  • Jiahan Wen

    (Faculty of Applied Science and Engineering, University of Toronto, Toronto M5S 1A4, Canada)

  • Zhaohong Wang

    (Department of Statistics, University of Illinois Urbana-Champaign, Champaign 61820, the United States of America)

  • Weiliang Hu

    (Social Science Research Institute, Duke University, Durham 27708, the United States of America)

  • Chenghan Wen

    (School of Applied Science & Engineering, Columbia University, New York 10027, the United States of America)

Abstract

In modern technology-driven organizations, strengthening cyber security and maintaining computer systems are critical priorities. Effective computer network fault diagnosis is essential for extending network availability, optimizing equipment utilization, minimizing latency, and ensuring service reliability while maintaining security. This process involves detecting state information from network devices during operation and identifying operational symptoms based on characteristic signals. By analyzing the sesymptoms along with other data, the system can accurately assess equipment status, pinpoint faulty servers, and diagnose the root cause of issues, leading to improved fault resolution and system performance. This study explores the application of Machine Learning Neural Network models in network fault diagnosis, focusing on the integration of genetic algorithms with a traditional Back propagation (BP) neural network. The analysis shows that this combined approach provides a more effective and scientifically robust method for predicting network faults.

Suggested Citation

  • Wenda Zhou & Jiahan Wen & Zhaohong Wang & Weiliang Hu & Chenghan Wen, 2025. "Application Of Computer Network Fault Diagnosis Based On Neural Network Model," Information Management and Computer Science (IMCS), Zibeline International Publishing, vol. 8(1), pages 10-13, April.
    • Handle: RePEc:zib:zbimcs:v:8:y:2025:i:1:p:10-13
    DOI: 10.26480/imcs.01.2025.10.13
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    References listed on IDEAS

    as
    1. Dong, Yutong & Jiang, Hongkai & Wang, Xin & Mu, Mingzhe & Jiang, Wenxin, 2024. "An interpretable multiscale lifting wavelet contrast network for planetary gearbox fault diagnosis with small samples," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    2. Li, Sheng & Jiang, Qiubo & Xu, Yadong & Feng, Ke & Wang, Yulin & Sun, Beibei & Yan, Xiaoan & Sheng, Xin & Zhang, Ke & Ni, Qing, 2023. "Digital twin-driven focal modulation-based convolutional network for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
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