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Dynamic management of a deep learning-based anomaly detection system for 5G networks

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dc.contributor.authorFernández Maimó, Lorenzo
dc.contributor.authorGil Pérez, Manuel
dc.contributor.authorGarcía Clemente, Félix Jesús
dc.contributor.authorMartínez Pérez, Gregorio
dc.contributor.authorHuertas Celdrán, Alberto
dc.contributor.departmentIngeniería y Tecnología de Computadores
dc.date.accessioned2026-01-20T09:23:12Z
dc.date.available2026-01-20T09:23:12Z
dc.date.copyright© Springer-Verlag GmbH Germany, part of Springer Nature 2018
dc.date.issued2018-05-05
dc.description.abstractFog and mobile edge computing (MEC) will play a key role in the upcoming fifth generation (5G) mobile networks to support decentralized applications, data analytics and management into the network itself by using a highly distributed compute model. Furthermore, increasing attention is paid to providing user-centric cybersecurity solutions, which particularly require collecting, processing and analyzing significantly large amount of data traffic and huge number of network connections in 5G networks. In this regard, this paper proposes a MEC-oriented solution in 5G mobile networks to detect network anomalies in real-time and in autonomic way. Our proposal uses deep learning techniques to analyze network flows and to detect network anomalies. Moreover, it uses policies in order to provide an efficient and dynamic management system of the computing resources used in the anomaly detection process. The paper presents relevant aspects of the deployment of the proposal and experimental results to show its performance.
dc.formatapplication/pdf
dc.format.extent15
dc.identifier.citationJournal of Ambient Intelligence and Humanized Computing, 2019, Vol. 10, pp. 3083–3097
dc.identifier.doihttps://doi.org/10.1007/s12652-018-0813-4
dc.identifier.eissn1868-5145
dc.identifier.issn1868-5137
dc.identifier.urihttp://hdl.handle.net/10201/189029
dc.languageeng
dc.publisherSpringer
dc.relationThis work has been partially supported by a Séneca Foundation grant within the Human Resources Researching Postdoctoral Program 2018, a postdoctoral INCIBE grant within the “Ayudas para la Excelencia de los Equipos de Investigación Avanzada en Ciberseguridad” Program, with code INCIBEI-2015-27352, the European Commission Horizon 2020 Programme under Grant Agreement Number H2020-ICT-2014-2/671672 - SELFNET (Framework for Self-Organized Network Management in Virtualized and Software Defined Networks), and the European Commission (FEDER/ERDF).
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s12652-018-0813-4
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectAnomay detection
dc.subjectVirtualization
dc.subject5G mobile networs
dc.subjectDeep learning
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleDynamic management of a deep learning-based anomaly detection system for 5G networks
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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