<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Computer networks, as one of the indispensable infrastructures in today’s world, play an extremely important role in industrial production and daily life. This paper revolves around the intelligent diagnosis of computer network data faults using machine learning methods. Firstly, the support vector machine network fault minimization principle is proposed based on the machine learning model, which leads to under-learning or over-learning when the samples are limited, or there is noise. Then, a loss function is introduced for the under-learning or over-learning problem to ensure that the support vector machine can accurately achieve fault diagnosis. Finally, the evaluation index of computational network fault diagnosis is constructed for the experimental purpose, and four algorithms are selected as the experimental control group to analyze the data. It is obtained that SVM and ANN models have high DR and low FAR. Their DR is 87.9% and 84.5%, respectively, while their FAR is only 5.4%. This further validates the superiority of SVM in computer network data fault detection. This study possesses low training time complexity and can overcome the problem of uneven distribution of the number of faulty and normal samples in network fault diagnosis to some extent.</p>
</abstract>

Computer networks, as one of the indispensable infrastructures in today’s world, play an extremely important role in industrial production and daily life. This paper revolves around the intelligent diagnosis of computer network data faults using machine learning methods. Firstly, the support vector machine network fault minimization principle is proposed based on the machine learning model, which leads to under-learning or over-learning when the samples are limited, or there is noise. Then, a loss function is introduced for the under-learning or over-learning problem to ensure that the support vector machine can accurately achieve fault diagnosis. Finally, the evaluation index of computational network fault diagnosis is constructed for the experimental purpose, and four algorithms are selected as the experimental control group to analyze the data. It is obtained that SVM and ANN models have high DR and low FAR. Their DR is 87.9% and 84.5%, respectively, while their FAR is only 5.4%. This further validates the superiority of SVM in computer network data fault detection. This study possesses low training time complexity and can overcome the problem of uneven distribution of the number of faulty and normal samples in network fault diagnosis to some extent.

Application of machine learning based models in computer network data

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Application of machine learning based models in computer network data"}

Computer networks, as one of the indispensable infrastructures in today’s world, play an extremely important role in industrial production and daily life. This...

Application of machine learning based models in computer network data

Data publikacji: 20 wrz 2023

Zakres stron: -

Otrzymano: 06 gru 2022

Przyjęty: 31 mar 2023

DOI: https://doi.org/10.2478/amns.2023.2.00345

Słowa kluczowe
Machine learning, Support vector machine, Computer network data, Loss function, Evaluation metrics

© 2023 Huixia Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of machine learning based models in computer network data

Data publikacji: 20 wrz 2023

Zakres stron: -

Otrzymano: 06 gru 2022

Przyjęty: 31 mar 2023

DOI: https://doi.org/10.2478/amns.2023.2.00345

Słowa kluczoweMachine learning, Support vector machine, Computer network data, Loss function, Evaluation metrics

© 2023 Huixia Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Machine learning, Support vector machine, Computer network data, Loss function, Evaluation metrics