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Use of Machine Learning Algorithms for Network Traffic Classification
dc.contributor.author | Nieto Antelo, Adrián | |
dc.contributor.author | Fernández, Diego | |
dc.contributor.author | Nóvoa, Francisco | |
dc.date.accessioned | 2023-11-08T16:23:23Z | |
dc.date.available | 2023-11-08T16:23:23Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | http://hdl.handle.net/2183/34111 | |
dc.description | Cursos e Congresos , C-155 | es_ES |
dc.description.abstract | [Abstract] In recent years, the complexity of threats utilizing the network as an attack vector has significantly increased. Traditional attack prevention and detection systems (IPS/IDS) based on signatures do not provide an acceptable level of security for many organizations. Furthermore, the volume of traffic on corporate networks has also grown exponentially, while quality of service requirements do not always allowfor deep inspection (at the application layer) of packets. The main objective of this work is to demonstrate that the application of machine learning techniques to the information of data flows circulating through the network allows for the satisfactory detection of malicious traffic. Specifically, this work is developed within an emerging network paradigm, such as software-defined networks | es_ES |
dc.description.sponsorship | This research has been funded by the Spanish Ministry of Economy and Competitiveness and European Union ERDF funds (Project PID2019-111388GB-I00). CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Conseller´ıa de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS) | |
dc.language.iso | eng | es_ES |
dc.publisher | Universidade da Coruña, Servizo de Publicacións | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111388GB-I00/ES/DETECCION TEMPRANA DE INTRUSIONES Y ANOMALIAS EN REDES DEFINIDAS POR SOFTWARE | es_ES |
dc.relation.uri | https://doi.org/10.17979/spudc.000024.48 | |
dc.rights | Attribution 4.0 International (CC BY 4.0) | es_ES |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es | * |
dc.subject | Aprendizaje automático | es_ES |
dc.subject | Flujo de datos | es_ES |
dc.subject | Detección de malware | es_ES |
dc.subject | Inteligencia artificial | es_ES |
dc.title | Use of Machine Learning Algorithms for Network Traffic Classification | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.conferenceTitle | VI Congreso Xove TIC: impulsando el talento científico. Octubre, 2023, A Coruña | es_ES |