Online classification of user activities using machine learning on network traffic

dc.contributor.authorLabayen Guembe, Víctor
dc.contributor.authorMagaña Lizarrondo, Eduardo
dc.contributor.authorMorató Osés, Daniel
dc.contributor.authorIzal Azcárate, Mikel
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzareneu
dc.date.accessioned2021-01-15T10:10:48Z
dc.date.available2021-01-15T10:10:48Z
dc.date.issued2020
dc.description.abstractThe daily deployment of new applications, along with the exponential increase in network traffic, entails a growth in the complexity of network analysis and monitoring. Conversely, the increasing availability and decreasing cost of computational capacity have increased the popularity and usability of machine learning algorithms. In this paper, a system for classifying user activities from network traffic using both supervised and unsupervised learning is proposed. The system uses the behaviour exhibited over the network and classifies the underlying user activity, taking into consideration all of the traffic generated by the user within a given time window. Those windows are characterised with features extracted from the network and transport layer headers in the traffic flows. A three-layer model is proposed to perform the classification task. The first two layers of the model are implemented using K-Means, while the last one uses a Random Forest to obtain the activity labels. An average accuracy of 97.37% is obtained, with values of precision and recall that allow online classification of network traffic for Quality of Service (QoS) and user profiling, outperforming previous proposals.en
dc.description.sponsorshipThis work was supported by Spanish MINECO through project PID2019-104451RB-C22.en
dc.format.extent12 p.
dc.format.mimetypeapplication/pdfen
dc.identifier.doi10.1016/j.comnet.2020.107557
dc.identifier.issn1389-1286
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/38957
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofComputer Networks, 2020, 181: 107557en
dc.relation.publisherversionhttps://doi.org/10.1016/j.comnet.2020.107557
dc.rights© 2020 The Author(s). This is an open access article under the CC BY licenseen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectNetwork trafficen
dc.subjectMachine learningen
dc.subjectUser activitiesen
dc.titleOnline classification of user activities using machine learning on network trafficen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublicationc521bf55-a1e7-47b2-ac98-5fbf8c286f7a
relation.isAuthorOfPublicationcd454059-725e-480a-b896-894e79f307a5
relation.isAuthorOfPublicationf829a159-0938-45d1-a352-d28fb297ed0b
relation.isAuthorOfPublication.latestForDiscoveryc521bf55-a1e7-47b2-ac98-5fbf8c286f7a

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