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Assessing Feature Selection Techniques for AI-based IoT Network Intrusion Detection

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dc.contributor.authorGarcía Merino, José Carlos
dc.contributor.authorTobarra Abad, María de los Llanos
dc.contributor.authorRobles Gómez, Antonio
dc.contributor.authorPastor Vargas, Rafael
dc.contributor.authorVidal Balboa, Pedro
dc.contributor.authorDionisio Rocha, André
dc.contributor.authorJardim Gonçalves, Ricardo
dc.coverage.spatialUniversidad de Lille, Francia
dc.coverage.temporal2025-06-25
dc.date.accessioned2025-05-09T07:05:46Z
dc.date.available2025-05-09T07:05:46Z
dc.date.issued2025-06
dc.descriptionThis is the Accepted Manuscript of an article that will soon be published by Springer in "Lecture Notes in Networks and Systems", when the conference proceedings are published. Este es el manuscrito aceptado de un artículo que Springer publicará próximamente en "Lecture Notes in Networks and Systems", cuando se publiquen las actas de la conferencia.
dc.description.abstractThe widespread adoption of Internet of Things (IoT) technology in rural areas has led to qualitative leaps in fields such as agriculture, livestock farming, and transportation, giving rise to the concept of Smart Rural. However, Smart Rural IoT ecosystems are often vulnerable to cyberattacks. Although Artificial Intelligence (AI) based intrusion detection systems offer an effective solution to protect these environments, IoT devices are typically constrained in terms of memory and computation capabilities, making it essential to optimise the computational burden of AI models. This work explores different feature selection techniques to develop compact and fast Random Forest models for anomaly detection in IoT environments. The obtained results demonstrate that appropriate feature selection can reduce model size and inference time by at least 45% and 8%, respectively, without compromising predictive performance.en
dc.description.versionversión final
dc.identifier.citationGarcía-Merino, J.C., Tobarra-Abad, Ll., Robles-Gómez, A., Pastor-Vargas, R., Dionisio-Rocha, A., Jardim-Gonçalves, R. (2025); Título: Assessing Feature Selection Techniques for AI-based IoT Network Intrusion Detection; Publicación: DCAI 2025 - Lecture Notes in Networks and Systems. ISSN: 2367-3370; Páginas 1-10
dc.identifier.issn2367-3370
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26526
dc.language.isoen
dc.publisherSpringer
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.congress22ª Conferencia Internacional en Computación Distribuida e Inteligencia Artificial. DCAI 2025
dc.relation.departmentSistemas de Comunicación y Control
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsInternet of Things (IoT)en
dc.subject.keywordssmart ruralen
dc.subject.keywordscybersecurityen
dc.subject.keywordsArtificial Intelligence (AI)en
dc.subject.keywordsIntrusion Detection System (IDS)en
dc.titleAssessing Feature Selection Techniques for AI-based IoT Network Intrusion Detectionen
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
person.familyNameTobarra Abad
person.familyNameRobles Gómez
person.familyNamePastor Vargas
person.givenNameMaría de los Llanos
person.givenNameAntonio
person.givenNameRafael
person.identifier.orcid0000-0003-2779-4042
person.identifier.orcid0000-0002-5181-0199
person.identifier.orcid0000-0002-4089-9538
relation.isAuthorOfPublicationb584f8a3-eb01-4a43-9ed7-5075b74224ae
relation.isAuthorOfPublication17556659-f434-4220-841d-aac35f492e62
relation.isAuthorOfPublicationf93103de-336d-47ac-886b-e2cbd425ed87
relation.isAuthorOfPublication.latestForDiscovery17556659-f434-4220-841d-aac35f492e62
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