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dc.contributor.authorMichelena, Álvaro
dc.contributor.authorAveleira Mata, Jose Antonio
dc.contributor.authorJove, Esteban
dc.contributor.authorBayón Gutiérrez, Martín
dc.contributor.authorNovais, Paulo
dc.contributor.authorFontenla-Romero, Óscar
dc.contributor.authorCalvo-Rolle, José Luis
dc.contributor.authorAlaiz Moretón, Héctor
dc.date.accessioned2024-04-30T18:30:58Z
dc.date.available2024-04-30T18:30:58Z
dc.date.issued2024
dc.identifier.citationMichelena, A., Aveleira‐Mata, J., Jove, E., Bayón‐Gutiérrez, M., Novais, P., Romero, O. F., ... & Aláiz‐Moretón, H. (2024). A novel intelligent approach for man‐in‐the‐middle attacks detection over internet of things environments based on message queuing telemetry transport. Expert Systems, 41(2), e13263. https://doi.org/10.1111/exsy.13263es_ES
dc.identifier.issn0266-4720
dc.identifier.issn1468-0394
dc.identifier.urihttp://hdl.handle.net/2183/36388
dc.description.abstract[Abstract]: One of the most common attacks is man-in-the-middle (MitM) which, due to its complex behaviour, is difficult to detect by traditional cyber-attack detection systems. MitM attacks on internet of things systems take advantage of special features of the protocols and cause system disruptions, making them invisible to legitimate elements. In this work, an intrusion detection system (IDS), where intelligent models can be deployed, is the approach to detect this type of attack considering network alterations. Therefore, this paper presents a novel method to develop the intelligent model used by the IDS, being this method based on a hybrid process. The first stage of the process implements a feature extraction method, while the second one applies different supervised classification techniques, both over a message queuing telemetry transport (MQTT) dataset compiled by authors in previous works. The contribution shows excellent performance for any compared classification methods. Likewise, the best results are obtained using the method with the highest computational cost. Thanks to this, a functional IDS will be able to prevent MQTT attacks.es_ES
dc.description.sponsorshipSpanish National Cybersecurity Institute (INCIBE) and developed Research Institute of Applied Sciences in Cybersecurity (RIASC). CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Álvaro Michelena's research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the “Formación de Profesorado Universitario” grant with reference FPU21/00932. Martín Bayón's research was supported by Universidad de León, under the “Programa Propio de Investigación de la Universidad de León 2021” grant. University of Leon. Support for ULE local research projects program. Ref. 2021/00145/001. Internal Code U252.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherWileyes_ES
dc.relationinfo:eu-repo/grantAgreement/MUNI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/FPU21%2F00932/ESes_ES
dc.relation.urihttps://doi.org/10.1111/exsy.13263es_ES
dc.rightsAtribución-NoComercial 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/es/*
dc.subjectArtificial neural networkses_ES
dc.subjectCybersecurityes_ES
dc.subjectDecision treeses_ES
dc.subjectK-nearest-neighbourses_ES
dc.subjectMan-in-the-middlees_ES
dc.subjectMessage queuing telemetry transportes_ES
dc.subjectPrincipal component analysises_ES
dc.subjectRandom forestes_ES
dc.titleA novel intelligent approach for man-in-the-middle attacks detection over internet of things environments based on message queuing telemetry transportes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleExpert Systemses_ES
UDC.volume41es_ES
UDC.issue2es_ES
UDC.startPagee13263es_ES
dc.identifier.doi10.1111/exsy.13263


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