Desarrollo de un modelo para la detección de ataques SSDF en redes de radio cognitiva móvil integrando técnicas de inteligencia artificial

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dc.contributor.advisorMateus Rojas, Armando
dc.contributor.advisorCubillos Sánchez, Rafael Orlando
dc.contributor.advisorCadena Muñoz, Ernesto
dc.contributor.authorYara Cifuentes, Lina María
dc.contributor.corporatenameUniversidad Santo Tomás
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000680630
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001040294
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001465749
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001470076
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=1az5o_IAAAAJ&hl=es&oi=ao
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=a527iHIAAAAJ&hl=es&oi=ao
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=qTycqC4AAAAJ&hl=es&oi=ao
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=mXktf50AAAAJ&hl=es&oi=ao
dc.contributor.orcidhttps://orcid.org/0000-0002-2399-4859
dc.contributor.orcidhttps://orcid.org/0000-0002-3364-9127
dc.contributor.orcidhttps://orcid.org/0000-0002-1086-3665
dc.contributor.orcidhttps://orcid.org/0009-0007-4913-2601
dc.date.accessioned2026-06-22T15:39:33Z
dc.date.available2026-06-22T15:39:33Z
dc.date.issued2026-06-19
dc.descriptionLas redes de radio cognitiva móvil (MCRNs) permiten el acceso dinámico al espectro mediante la técnica de detección cooperativa del espectro (CSS), pero presentan vulnerabilidades frente a ataques de falsificación de datos de detección del espectro (SSDF). Diversos estudios han abordado este problema mediante técnicas de aprendizaje automático, modelos de confianza y métodos híbridos. Este artículo presenta los principales resultados de un modelo híbrido que integra la máquina de vectores de soporte (SVM), el algoritmo K-Nearest Neighbors (KNN) y un sistema de reputación basado en la distribución Beta para identificar usuarios maliciosos. La validación experimental mediante radio definida por software (SDR) demuestra probabilidades de detección superiores al 90 %, una reducción significativa de falsas alarmas y una mayor robustez en condiciones de movilidad y con bajos valores de relación señal-ruido (SNR).
dc.description.abstractMobile Cognitive Radio Networks (MCRNs) enable dynamic spectrum access through the Cooperative Spectrum Sensing (CSS) technique; however, they present vulnerabilities to Spectrum Sensing Data Falsification (SSDF) attacks. Various studies have addressed this problem using machine learning techniques, trust models, and hybrid methods. This article presents the main results of a hybrid model that integrates Support Vector Machines (SVM), the K-Nearest Neighbors (KNN) algorithm, and a reputation system based on the Beta distribution to identify malicious users. Experimental validation using Software Defined Radio (SDR) demonstrates detection probabilities above 90%, a significant reduction in false alarms, and greater robustness under mobility conditions and low Signal-to-Noise Ratio (SNR) values.
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagister en Ingeniería Electrónicaspa
dc.format.mimetypeapplication/pdf
dc.identifier.citationYara Cifuentes, L. M., Cadena Muñoz, E., Cubillos Sánchez, R y Mateus Rojas, A (2026). Desarrollo de un modelo para la detección de ataques SSDF en redes de radio cognitiva móvil integrando técnicas de inteligencia artificial. [TRabajo de Maestría, Universidad Santo Tomás]. Repositorio Institucional
dc.identifier.instnameinstname:Universidad Santo Tomásspa
dc.identifier.reponamereponame:Repositorio Institucional Universidad Santo Tomásspa
dc.identifier.repourlrepourl:https://repository.usta.edu.cospa
dc.identifier.urihttp://hdl.handle.net/11634/72745
dc.language.isospa
dc.publisherUniversidad Santo Tomásspa
dc.publisher.branchCRAI-USTA Bogotá
dc.publisher.facultyFacultad de Ingeniería Electrónicaspa
dc.publisher.programMaestría Ingeniería Electrónicaspa
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dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Colombiaen
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.subject.keywordMobile Cognitive Radio Networks
dc.subject.keywordSSDF Attack
dc.subject.keywordCooperative Spectrum Sensing
dc.subject.keywordMachine Learning
dc.subject.keywordReputation Systems
dc.subject.keywordSDR
dc.subject.lembIngeniería electrónica
dc.subject.lembRedes de radio cognitiva
dc.subject.lembCiberseguridad
dc.subject.proposalMCRN
dc.subject.proposalSSDF
dc.subject.proposalaprendizaje automático
dc.subject.proposalSVM
dc.subject.proposalKNN
dc.subject.proposalreputación
dc.subject.proposalSDR
dc.titleDesarrollo de un modelo para la detección de ataques SSDF en redes de radio cognitiva móvil integrando técnicas de inteligencia artificial
dc.typemaster thesis
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driveinfo:eu-repo/semantics/masterThesis
dc.type.localTesis de maestríaspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersion

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