A novel methodology for the automated detection and classification of networking anomalies

The active growth and dynamic nature of cellular networks makes challenging accommodating end-users with flawless quality of service. 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 show that our method can automatically identify and classify networking anomalies, so to enable timely and precise troubleshooting actions.