Machine Learning Based Network Status Detection and Fault Localization

Ayse Rumeysa Mohammed, Shady A. Mohammed, David Cote, Shervin Shirmohammadi
2021 IEEE Transactions on Instrumentation and Measurement  
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 paper, we propose a Machine Learning (ML) method that can automatically detect the status of a network and localize faults. Our method uses Decision Tree, Gradient Boosting (GB), and XGBoost (XGB) ML
more » ... lgorithms 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 non-faulty. Experimental results show that our method yields accuracies of up to 99% on a dataset collected through an emulated network.
doi:10.1109/tim.2021.3094223 fatcat:zq7wiscuhffx7cxyixb6yrnlmy