Cyberattacks Predictions Workflow using Machine Learning

dc.creatorBarrera Pérez, Carlos Eduardo
dc.creatorSerrano, Jairo E.
dc.creatorMartinez-Santos, Juan Carlos
dc.date.accessioned2023-07-21T16:23:36Z
dc.date.accessioned2023-09-06T15:44:07Z
dc.date.available2023-07-21T16:23:36Z
dc.date.available2023-09-06T15:44:07Z
dc.date.created2023-07-21T16:23:36Z
dc.date.issued2021
dc.identifierPérez, 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.
dc.identifierhttps://hdl.handle.net/20.500.12585/12335
dc.identifier10.1109/ICMLANT53170.2021.9690527
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio Universidad Tecnológica de Bolívar
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8682647
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.
dc.languageeng
dc.publisherCartagena de Indias
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.sourceProceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021
dc.titleCyberattacks Predictions Workflow using Machine Learning


Este ítem pertenece a la siguiente institución