Aplicación de técnicas de aprendizaje automático (no supervisado) para la detección de anomalías en infraestructuras críticas cibernéticas

dc.contributorDonoso Meisel, Yezid Enrique
dc.contributorLozano Garzón, Carlos Andrés
dc.contributorMontoya Orozco, Germán Adolfo
dc.creatorPinto Rojas, Yuri Andrea
dc.date.accessioned2023-07-14T14:45:19Z
dc.date.accessioned2023-09-07T02:17:09Z
dc.date.available2023-07-14T14:45:19Z
dc.date.available2023-09-07T02:17:09Z
dc.date.created2023-07-14T14:45:19Z
dc.date.issued2023-05-25
dc.identifierhttp://hdl.handle.net/1992/68449
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8729133
dc.description.abstractLas Infraestructuras Críticas (ICs) desempeñan un papel fundamental en el soporte y funcionamiento de servicios esenciales como el sistema de transporte, las telecomunicaciones y el tratamiento de agua, entre otros. No obstante, la conexión de estas infraestructuras a nuevas tecnologías ha aumentado la superficie de ataque y por lo tanto, su protección se ha convertido en una prioridad en términos de seguridad nacional. Los ciberataques se han vuelto más sofisticados, lo que ha permitido a los criminales evadir los sistemas de seguridad convencionales, planteando así un desafío en la detección de mencionados ataques. En este contexto, las técnicas de aprendizaje automático (ML, por sus siglas en inglés) ofrecen la capacidad de abordar amenazas de mayor alcance y diversidad. Sin embargo, la detección de ataques día cero y los recursos necesarios para implementar soluciones basadas en ML en entornos reales representan preocupaciones para los operadores de las ICs. Este trabajo tiene como objetivo aplicar técnicas de aprendizaje automático no supervisado para la detección de anomalías en ICs.
dc.languagespa
dc.publisherUniversidad de los Andes
dc.publisherMaestría en Seguridad de la Información
dc.publisherFacultad de Ingeniería
dc.publisherDepartamento de Ingeniería Sistemas y Computación
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dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rightshttps://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleAplicación de técnicas de aprendizaje automático (no supervisado) para la detección de anomalías en infraestructuras críticas cibernéticas
dc.typeTrabajo de grado - Maestría


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