Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Rzayeva L. Myrzatay A. Abitova G. Sarinova A. Kulniyazova K. Saoud B. Shayea I.
September 2023 Multidisciplinary Digital Publishing Institute (MDPI)

Electronics (Switzerland)
2023 #12 Issue 18

Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

Cisco switches , decision tree , failure prediction , LAN , machine learning methods , random forest , SVC , SVM

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Department of Intelligent Systems and Cybersecurity, Astana IT University, Astana, 010000, Kazakhstan
Department of Computer Science, Korkyt Ata Kyzylorda University, Kyzylorda, 120000, Kazakhstan
Department of Systems Analysis and Control, L.N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
Electrical Engineering Department, Sciences and Applied Sciences Faculty, Bouira University, Bouira, 10000, Algeria
Electronics & Communications Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul, 34469, Turkey

Department of Intelligent Systems and Cybersecurity
Department of Computer Science
Department of Systems Analysis and Control
Electrical Engineering Department
Electronics & Communications Engineering Department

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