Work

A Novel Methodology for the Automated Detection and Classification of Networking Anomalies

Year: 2020

Type: conference-paper

Abstract: 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.

Source: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

DOI: 10.1109/infocomwkshps50562.2020.9162710

Authors: Mohamed Moulay, Rafael Garcia Leiva, Pablo J. Rojo Maroni, Javier Lazaro, Vincenzo Mancuso, Antonio Fernandez Anta

Institutions: Universidad Carlos III de Madrid, IMDEA Networks, Nokia (Spain)

Language: English

Topic: Software System Performance and Reliability

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: Ministerio de Economía y Competitividad