Аннотации:
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.
