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Cyberattacks Predictions Workflow using Machine Learning

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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
dc.contributor.authorBarrera Pérez, Carlos Eduardo
dc.contributor.authorMartínez Santos, Juan Carlos
dc.contributor.authorSerrano Castañeda, Jairo Enrique
dc.date.accessioned2023-07-21T16:23:36Z
dc.date.available2023-07-21T16:23:36Z
dc.date.issued2021
dc.date.submitted2023
dc.description.abstractThis research aims to validate the effectiveness of a machine learning model composed of three classifiers: decision tree, logistic regression, and support vector machines. Through the design of a workflow, we demonstrate the effectiveness of the model. First, we execute a network attack, and then monitoring, processing, storage, visualization, and data transfer tools are implemented to create the most realistic environment possible and obtain more accurate predictions. © 2021 IEEE.spa
dc.format.extent6 páginas
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationPérez, C. E. B., Serrano, J. E., & Martinez-Santos, J. C. (2021, December). Cyberattacks Predictions Workflow using Machine Learning. In 2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) (pp. 1-6). IEEE.spa
dc.identifier.doi10.1109/ICMLANT53170.2021.9690527
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/12335
dc.language.isoengspa
dc.publisher.placeCartagena de Indiasspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceProceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021spa
dc.subject.armarcLEMB
dc.subject.keywordsDenial-Of-Service Attack;spa
dc.subject.keywordsDDoS;spa
dc.subject.keywordsAttackspa
dc.titleCyberattacks Predictions Workflow using Machine Learningspa
dc.typeArtículo de revistaspa
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dc.type.hasversioninfo:eu-repo/semantics/draftspa
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