Machine Learning-Based Network Status Detection and Fault Localization
Ayse Rumeysa Mohammed, Shady Mohammed, David Côté, Shervin Shirmohammadi
Abstract
Although the autonomous detection of network status and localization of network faults can be a valuable tool for network and service operators, very few works have investigated this subject. As a result in today's networks, fault detection and localization remains a mostly manual process. In this article, we propose a machine learning (ML) method that can automatically detect the status of a network and localize faults. Our method uses the decision tree, gradient boosting (GB), and extreme GB ML algorithms to detect the network status as normal, congestion, and network fault. In comparison, existing related work can at best classify the network status as faulty or nonfaulty. Experimental results show that our method yields accuracies of up to 99% on a dataset collected through an emulated network.