Publicación:
Anomaly Detection in Smart Rural IoT Systems

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dc.contributor.authorFernández Morales, Enrique
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.authorSarraipa, Joao
dc.coverage.spatialUniversidad de Lille, Francia
dc.coverage.temporal2025-06
dc.date.accessioned2025-05-12T07:46:39Z
dc.date.available2025-05-12T07:46:39Z
dc.date.issued2025-06
dc.descriptionThis is the Accepted Manuscript of an article that will soon be published by Springer in "Main Track", when the conference proceedings are published. Este es el manuscrito aceptado de un artículo que Springer publicará próximamente en "Main Track", cuando se publiquen las actas de la conferencia.
dc.description.abstractThe main contribution of this work is based on the presentation of new AI models for the detection of attacks, within IoT systems using an extensive and complete dataset. In this context, we evaluate not only the performance of the models in terms of detection of attacks, but also their resource consumption, such as the time needed to analyze a sample, the consumption of computing cycles to analyze a sample, as well as the hard disk usage to store the AI models. Its application is oriented to the context of IoT systems in rural environments, where devices deployed in these environments usually have strong restrictions on these resources. Our results indicate that the OPTIMIST-LSTM model offers the best balance between accuracy and generalization, whereas XAI-IoT stands out for its computational efficiency, making them the most suitable for implementation in IoT infrastructures with limited resources.en
dc.description.versionversión final
dc.identifier.citationEnrique Fernández-Morales, Llanos Tobarra, Antonio Robles-Gómez, Rafael Pastor-Vargas, Pedro Vidal-Balboa, Joao Sarraipa. (2025); Título: Anomaly Detection in Smart Rural IoT Systems; Publicación: DCAI - Main Track. ISSN: 2172-766X; Páginas 1-10
dc.identifier.issn2172-766X
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26533
dc.language.isoen
dc.publisherSpringer
dc.relation.centerFacultades y escuelas::E.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/embargoedAccess
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.titleAnomaly Detection in Smart Rural IoT Systemsen
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.latestForDiscoveryb584f8a3-eb01-4a43-9ed7-5075b74224ae
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