Selección de una Técnica de Aprendizaje de Máquina para la Detección de Ataques de Red.

Morgado Gómez, Ivonne Marcela

Selección de una Técnica de Aprendizaje de Máquina para la Detección de Ataques de Red.

dc.contributor.advisor	Arévalo Herrera, Juliana Alejandra
dc.contributor.author	Morgado Gómez, Ivonne Marcela
dc.contributor.corporatename	Universidad Santo Tomás	spa
dc.contributor.cvlac	https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000759813
dc.contributor.orcid	https://orcid.org/0000-0001-7401-4286
dc.date.accessioned	2024-09-20T13:40:33Z
dc.date.available	2024-09-20T13:40:33Z
dc.date.issued	2024-09
dc.description	En esta investigación, con el propósito de diseñar un sistema de Machine Learning para la detección de ataques de red, se realizó en primera instancia una exploración de las investigaciones actuales y bases de datos enfocadas en la ciberseguridad. Posteriormente se escogieron tres de ellas para identificar su estructura e idoneidad, para ser la base de la construcción del modelo de aprendizaje de máquina. Luego de realizar la comparación entre estas candidatas se construyeron diferentes modelos de Machine Learning y se comparó su desempeño a partir de métricas asociadas con sistemas de clasificación supervisado. Finalmente se identificó la mejor opción para aprovechar las fortalezas de los modelos de Machine learning mediante la construcción de un modelo final el cual brinda las mejores métricas de desempeño.	spa
dc.description.abstract	In this research, with the purpose of designing a Machine Learning system for network attack detection, an exploration of current research and databases focused on cybersecurity was carried out. Subsequently, three of them were chosen to identify their structure and suitability, to be the foundation for the construction of the machine learning model. After performing comparisons between these candidates, different Machine Learning models were built and their performance was compared based on metrics associated with supervised classification systems. Finally, the best option to take advantage of the strengths of the machine learning models was identified by building a final model which provides the best performance metrics.	spa
dc.description.degreelevel	Maestría	spa
dc.description.degreename	Magister en Telecomunicaciones y Regulación tic	spa
dc.format.mimetype	application/pdf
dc.identifier.citation	Morgado Gómez, I. M. (2024). Selección de una Técnica de Aprendizaje de Máquina para la Detección de Ataques de Red. [Trabajo Maestría, Universidad Santo Tomás]. Repositorio Institucional	spa
dc.identifier.instname	instname:Universidad Santo Tomás	spa
dc.identifier.reponame	reponame:Repositorio Institucional Universidad Santo Tomás	spa
dc.identifier.repourl	repourl:https://repository.usta.edu.co	spa
dc.identifier.uri	http://hdl.handle.net/11634/57746
dc.language.iso	spa
dc.publisher	Universidad Santo Tomás	spa
dc.publisher.branch	CRAI-USTA Bogotá	spa
dc.publisher.faculty	Facultad de Ingeniería de Telecomunicaciones	spa
dc.publisher.program	Maestría Telecomunicaciones y Regulación TIC	spa
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dc.rights	CC0 1.0 Universal
dc.rights.accessrights	info:eu-repo/semantics/openAccess
dc.rights.coar	http://purl.org/coar/access_right/c_abf2	spa
dc.rights.local	Abierto (Texto Completo)	spa
dc.rights.uri	http://creativecommons.org/publicdomain/zero/1.0/
dc.subject.keyword	Machine Learning	spa
dc.subject.keyword	XGBoost	spa
dc.subject.keyword	Voting Classifier	spa
dc.subject.keyword	Neural Network	spa
dc.subject.keyword	Cybersecurity	spa
dc.subject.lemb	Ingeniería de Telecomunicaciones	spa
dc.subject.lemb	Aprendizaje -- Máquinas	spa
dc.subject.lemb	Diseño de Sistema	spa
dc.subject.lemb	Bases de Datos	spa
dc.subject.proposal	Machine Learning	spa
dc.subject.proposal	XGBoost	spa
dc.subject.proposal	Redes Neuronales	spa
dc.subject.proposal	Voting Classifier	spa
dc.subject.proposal	Ataques	spa
dc.subject.proposal	Red	spa
dc.title	Selección de una Técnica de Aprendizaje de Máquina para la Detección de Ataques de Red.	spa
dc.type.coar	http://purl.org/coar/resource_type/c_bdcc
dc.type.coarversion	http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.drive	info:eu-repo/semantics/masterThesis
dc.type.local	Tesis de maestría	spa
dc.type.version	info:eu-repo/semantics/acceptedVersion