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eXplicability AI (XAI) for Attack Detection toward Smart Rural Applications

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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.authorHernández Berlinches, Roberto
dc.contributor.authorSarraipa, Joao
dc.contributor.funderFinanciado por INCIBE en el contexto del Plan de Recuperación, Transformación y Resiliencia de la Unión Europea (NextGenerationEU/PRTR)
dc.date.accessioned2025-10-24T16:31:53Z
dc.date.available2025-10-24T16:31:53Z
dc.date.issued2025-10-20
dc.descriptionThe registered version of this article, first published in Internet of Things, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.iot.2025.101804
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Internet of Things, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.iot.2025.101804
dc.descriptionCátedra Internacional "Smart Rural IoT and Secured Environments" (C56.23). Financiado por INCIBE en el contexto del Plan de Recuperación, Transformación y Resiliencia de la Unión Europea (NextGenerationEU/PRTR)
dc.description.abstractThis research evaluates the performance and computational efficiency of various AI models for intrusion detection in IoT environments, with the goal of enabling future deployment in Smart Rural scenarios. Leveraging the massive NF-UQ-NIDS-v2 dataset-comprising over 76 million labeled NetFlow records across 21 traffic classes-we benchmark five models, ranging from classical machine learning algorithms to deep learning architectures, across both high-performance and low-performance execution setups. The analysis covers standard classification metrics (accuracy, precision, recall, F1-score) and detailed resource usage indicators, including inference time, memory footprint, CPU cycles, and energy consumption per batch. Additionally, explainable AI techniques (SHAP and LIME) are employed to investigate model behavior and feature relevance under real-world constraints. Results show that classical models, particularly Random Forest and Decision Tree, achieve top-tier detection accuracy while maintaining minimal computational demands, making them strong candidates for constrained deployments. Deep learning models deliver comparable predictive performance but incur significantly higher resource consumption, requiring further optimization for practical use. Overall, this work provides a comprehensive evaluation framework and practical insights for selecting efficient and interpretable AI-based intrusion detection systems for rural and low-resource infrastructures.en
dc.description.versionversión final
dc.identifier.citationEnrique Fernández-Moralesa, Llanos Tobarra, Antonio Robles-Gómez, Rafael Pastor-Vargas, Roberto Hernández, Joao Sarraipa. eXplicability AI (XAI) for Attack Detection toward Smart Rural Applications. Internet of Things; Engineering Cyber Physical Human Systems. 2025.
dc.identifier.doihttps://doi.org/10.1016/j.iot.2025.101804
dc.identifier.issn2542-6605
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30615
dc.journal.titleInternet of Things
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
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.subject1203.04 Inteligencia artificial
dc.subject.keywordsInternet of Things (IoT)en
dc.subject.keywordscybersecurityen
dc.subject.keywordsanomaly detectionen
dc.subject.keywordsdeep learning,en
dc.subject.keywordseXplicability AI (XAI)en
dc.titleeXplicability AI (XAI) for Attack Detection toward Smart Rural Applicationsen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationb584f8a3-eb01-4a43-9ed7-5075b74224ae
relation.isAuthorOfPublication17556659-f434-4220-841d-aac35f492e62
relation.isAuthorOfPublicationf93103de-336d-47ac-886b-e2cbd425ed87
relation.isAuthorOfPublication4771643d-95d5-4693-be4c-933bcea7bdeb
relation.isAuthorOfPublication.latestForDiscoveryb584f8a3-eb01-4a43-9ed7-5075b74224ae
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