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Network Data Flow Clustering based on Unsupervised Learning
dc.contributor.author | López-Vizcaíno, Manuel F. | |
dc.contributor.author | Dafonte, Carlos | |
dc.contributor.author | Nóvoa, Francisco | |
dc.contributor.author | Garabato, D. | |
dc.contributor.author | Álvarez, M. A. | |
dc.contributor.author | Fernández, Diego | |
dc.date.accessioned | 2024-02-05T14:03:05Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | M. López-Vizcaíno, C. Dafonte, F. J. Novoa, D. Garabato, M. A. Álvarez and D. Fernández, "Network Data Flow Clustering based on Unsupervised Learning," 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA, 2019, pp. 1-5, doi: 10.1109/NCA.2019.8935041. | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/35398 | |
dc.description.abstract | [Abstract]: Network communication data analysis is crucial in order to provide an adequate security level in computer infrastructures. As the volume of data and the number of features rise, the difficulties associated with its study also increase. A possible approach to this problem based on Artificial Intelligence techniques is to perform clustering, creating groups of similar characteristics. Self-Organized Maps are an artificial neural networks which perform dimensionality reduction as well as clustering over numerical data. In this paper, we propose a mixed numerical-categorical version of Self-Organized Maps algorithm applied to network communication data. Moreover a study of its enhancement with the inclusion of time dependent features is performed. The technique is tested with a well known dataset for Intrusion Detection Systems and it provides a technique to create different clusters or groups of different types of traffic by means of network information. | es_ES |
dc.description.sponsorship | Part of this work was supported by the Xunta de Galicia (ED431B 2018/42), the European Regional Development Fund-ERDF) and Spanish MECD FPU16/03827; we also used IT infrastructure that was acquired through the RTI2018-095076-B-C22 and ESP2016-80079-C2-2-R projects, financed by the Spanish MICINN and MINECO Ministries. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2018/42 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | es_ES |
dc.relation | info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPU16%2F03827/ES/ | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095076-B-C22/ES/MINERIA DE DATOS DE GAIA PARA ESTUDIAR LA VIA LACTEA II | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ESP2016-80079-C2-2-R/ES/MINERIA DE DATOS DE GAIA PARA ESTUDIAR LA VIA LACTEA | es_ES |
dc.relation.uri | https://doi.org/10.1109/NCA.2019.8935041 | es_ES |
dc.rights | © 2019, IEEE | es_ES |
dc.subject | Communication networks | es_ES |
dc.subject | Intrusion detection | es_ES |
dc.subject | Feature extraction | es_ES |
dc.subject | Computer security | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject | Clustering algorithms | es_ES |
dc.subject | Self-Organizing Maps | es_ES |
dc.subject | IDS | es_ES |
dc.subject | Network Security | es_ES |
dc.subject | Categorical SOM | es_ES |
dc.subject | Visualization | es_ES |
dc.subject | Unsupervised Clustering | es_ES |
dc.title | Network Data Flow Clustering based on Unsupervised Learning | 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/embargoedAccess | es_ES |
dc.date.embargoEndDate | 9999-99-99 | es_ES |
dc.date.embargoLift | 10007-06-07 | |
dc.identifier.doi | 10.1109/NCA.2019.8935041 | |
UDC.conferenceTitle | IEEE International Symposium on Network Computing and Applications (NCA) | es_ES |