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

TL;DR

This paper introduces MonTrees, a machine learning-based system for automated detection and classification of cellular network anomalies, improving troubleshooting efficiency in dynamic cellular environments.

Contribution

It presents a novel hybrid machine learning methodology combining supervised and unsupervised algorithms for anomaly detection and classification in cellular networks.

Findings

Effective automatic identification of network anomalies.

High accuracy in classifying different types of anomalies.

Applicable to real-world operational mobile networks.

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.