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arXiv · 2108.13156

MonTrees: Automated Detection and Classification of Networking Anomalies in Cellular Networks

Mohamed Moulay·Rafael Garcia Leiva·Pablo J. Rojo Maroni·Vincenzo Mancuso·Antonio Fernandez Anta·Ali Safari Khatouni
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Abstract

The active growth and dynamic nature of cellular networks makes network troubleshooting challenging. Identification of network problems leveraging on machine learning has gained a lot of visibility in the past few years, resulting in dramatically improved cellular network services. In this paper, we present a novel methodology to automate the fault identification process in a cellular network and to classify network anomalies, which combines supervised and unsupervised machine learning algorithms. Our experiments using real data from operational commercial mobile networks obtained through drive-test measurements as well as via the MONROE platform show that our method can automatically identify and classify networking anomalies, thus enabling timely and precise troubleshooting actions.

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Mohamed Moulay, Rafael Garcia Leiva, Pablo J. Rojo Maroni, Vincenzo Mancuso, Antonio Fernandez Anta, Ali Safari Khatouni. 2021-07-12. MonTrees: Automated Detection and Classification of Networking Anomalies in Cellular Networks. https://arxiv.org/abs/2108.13156

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