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dc.contributor.authorMoulay, Mohamed
dc.contributor.authorGarcía, Rafael
dc.contributor.authorMancuso, Vincenzo 
dc.contributor.authorFernández Anta, Antonio 
dc.contributor.authorRojo, Pablo
dc.contributor.authorLazaro, Javier
dc.date.accessioned2021-07-13T09:41:53Z
dc.date.available2021-07-13T09:41:53Z
dc.date.issued2020-07-06
dc.identifier.urihttp://hdl.handle.net/20.500.12761/804
dc.description.abstractThe 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.
dc.language.isoeng
dc.titleA Novel Methodology for the Automated Detection and Classification of Networking Anomaliesen
dc.typeconference object
dc.conference.date6-9 July 2020
dc.conference.placeToronto
dc.conference.titleThe 3rd International Workshop on Network Intelligence (NI 2020): Learning and Optimizing Future Networks, in conjunction with IEEE INFOCOM 2020*
dc.event.typeworkshop
dc.pres.typepaper
dc.type.hasVersionAM
dc.rights.accessRightsopen access
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/2126


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