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http://hdl.handle.net/10662/21277
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Campo DC | Valor | idioma |
---|---|---|
dc.contributor.author | Galeano Brajones, Jesús | - |
dc.contributor.author | Chidean, Mihaela I. | - |
dc.contributor.author | Luna, Francisco | - |
dc.contributor.author | Carmona Murillo, Javier Domingo | - |
dc.date.accessioned | 2024-05-22T07:19:57Z | - |
dc.date.available | 2024-05-22T07:19:57Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 0140-3664 | - |
dc.identifier.uri | http://hdl.handle.net/10662/21277 | - |
dc.description.abstract | The continuous increase in the number of devices connected to the Internet, together with the growth of applications and services, has made the tasks of network traffic analysis and classification essential in any environment. The deployment of 5G networks has prompted the research community to establish the pillars of Next-Generation Networks. These include intelligent systems, providing the network with intelligence in management and security tasks. In addition, these tasks require mechanisms capable of characterizing traffic in order to make network decisions. In this context, this paper proposes a novel methodology for processing network traffic using the L-moments theory and Machine Learning algorithms. This methodology is robust to outliers, requires few data to characterize the flows and subsequently fit the classification models. The results show that L-moments are particularly useful for processing network flows, and the classification algorithms obtain very high-quality results. Moreover, we show that the considered statistical tools also allow for a better understanding of the attack behaviour, leading the way to the improvement of the feature selection in similar problems. | es_ES |
dc.description.sponsorship | This research was funded in part by the European Union NextGenerationEU/PRTR, grant TED2021-131699B-I00 (AEI/FEDER,UE), by the Spanish Ministry of Science and Innovation, grant numbers PID2020-112545RB-C54 and PDC2022-133900-I00, by the Regional Government of Extremadura, Spain, grant IB18003, and by the Univ. Rey Juan Carlos Program for Research Promotion and Development (Ref. F920 and “AYUDA PUENTE 2022, URJC” Ref. F931) | es_ES |
dc.format.extent | 7 p. | es_ES |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Network traffic analysis | es_ES |
dc.subject | L-moments theory | es_ES |
dc.subject | Intelligent network management | es_ES |
dc.subject | 6G | es_ES |
dc.subject | Machine Learning | es_ES |
dc.subject | Análisis del tráfico de red | es_ES |
dc.subject | Teoría de los momentos-L | es_ES |
dc.subject | Gestión de red inteligente | es_ES |
dc.subject | Aprendizaje automático | es_ES |
dc.title | A novel approach for flow analysis in software-based networks using L-moments theory | es_ES |
dc.type | article | es_ES |
dc.description.version | peerReviewed | es_ES |
europeana.type | TEXT | en_US |
dc.rights.accessRights | openAccess | es_ES |
dc.subject.unesco | 1203.10 Enseñanza Con Ayuda de Ordenador | es_ES |
dc.subject.unesco | 1207.01 Análisis de Actividades | es_ES |
europeana.dataProvider | Universidad de Extremadura. España | es_ES |
dc.identifier.bibliographicCitation | Galeano-Brajones, J., Chidean, M.I., Luna, F., Carmona-Murillo, J. (2023). A novel approach for flow analysis in software-based networks using L-moments theory. Computer Communications, 201, 116-122. https://doi.org/10.1016/j.comcom.2023.01.022 | es_ES |
dc.type.version | publishedVersion | es_ES |
dc.contributor.affiliation | Universidad Rey Juan Carlos | es_ES |
dc.contributor.affiliation | Universidad de Extremadura. Departamento de Ingeniería de Sistemas Informáticos y Telemáticos | es_ES |
dc.contributor.affiliation | Universidad de Málaga | - |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0140366423000300?via%3Dihub | es_ES |
dc.identifier.doi | 10.1016/j.comcom.2023.01.022 | - |
dc.identifier.publicationtitle | Computer Communications | es_ES |
dc.identifier.publicationfirstpage | 116 | es_ES |
dc.identifier.publicationlastpage | 122 | es_ES |
dc.identifier.publicationvolume | 201 | es_ES |
dc.identifier.orcid | 0000-0001-8691-8944 | es_ES |
dc.identifier.orcid | 0000-0002-3910-876X | es_ES |
Colección: | DISIT - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
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j_comcom_2023_01_022.pdf | 2,08 MB | Adobe PDF | Descargar |
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