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Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview

dc.contributor.authorPayares, Esteban
dc.contributor.authorMartínez-Santos, Juan Carlos
dc.date.accessioned2022-01-28T20:08:39Z
dc.date.available2022-01-28T20:08:39Z
dc.date.issued2021-03-01
dc.date.submitted2022-01-28
dc.identifier.citationE. D. Payares, J. C. Martinez-Santos, "Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview," Proc. SPIE 11699, Quantum Computing, Communication, and Simulation, 116990B (5 March 2021); doi: 10.1117/12.2593297spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/10426
dc.description.abstractIn recent years, we have seen an increase in computer attacks through our communication networks worldwide, whether due to cybersecurity systems' vulnerability or their absence. This paper presents three quantum models to detect distributed denial of service attacks. We compare Quantum Support Vector Machines, hybrid Quantum- Classical Neural Networks, and a two-circuit ensemble model running parallel on two quantum processing units. Our work demonstrates quantum models' e ectiveness in supporting current and future cybersecurity systems by obtaining performances close to 100%, being 96% the worst-case scenario. It compares our models' performance in terms of accuracy and consumption of computational resources.spa
dc.format.extent11 Páginas
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceQuantum Computing, Communication, and Simulationspa
dc.titleQuantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overviewspa
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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasversioninfo:eu-repo/semantics/restrictedAccessspa
dc.identifier.doi10.1117/12.2593297
dc.subject.keywordsQuantum computingspa
dc.subject.keywordsQuantum machine learningspa
dc.subject.keywordsQuantum Processing Unitsspa
dc.subject.keywordsCybersecurityspa
dc.subject.keywordsDDoS attacksspa
dc.subject.keywordsSmart Intrusion Detection Systemsspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.subject.armarcLEMB
dc.type.spahttp://purl.org/coar/resource_type/c_2df8fbb1spa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_2df8fbb1spa


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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.