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An Efficient Feature Selection Framework Using Genetic Algorithms for AI-Driven IDS in IoT Environments

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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.date.accessioned2025-10-03T07:42:42Z
dc.date.available2025-10-03T07:42:42Z
dc.date.issued2025-12-01
dc.descriptionArtículo perteneciente al próximo Congreso CSCI 2025 "International Conference on Computational Science and Computational Intelligence Proceedings" que se celebrará el 3-5 de Diciembre de 2025 en Las Vegas (USA)
dc.descriptionProyecto estratégico: Analysis of mobile applications from the perspective of data protection: Cyber-protection and Cyber-risks of citizen information'' y Cátedra Internacional "Smart Rural IoT and Secured Environments''
dc.description.abstractThe increasing concern over Internet of Things (IoT) cybersecurity, driven by the growing number of connected devices and their vulnerability to attacks, has intensified the demand for effective Intrusion Detection Systems (IDSs). In recent years Artificial Intelligence (AI) solutions has emerged as a common approach in modern cybersecurity systems, offering powerful tools for detecting complex patterns and threats. However, the limited computing capabilities of IoT environments require models that are not only accurate but also resource-efficient. Feature selection (FS) plays a key role in this context, but traditional techniques such as the Chi-2 test and mutual information focus only on predictive performance, overlooking important practical concerns like inference time and model size. In this work, we propose a novel FS approach based on a custom scoring function that combines accuracy with penalties for inference time and model size. This metric is integrated into a Genetic Algorithm framework to guide the search for optimal feature subsets. The proposed method is evaluated on the NF-ToN-IoT-v2 dataset against standard techniques. Results demonstrate that our approach achieves competitive predictive performance while significantly reducing inference time and model size, highlighting the relevance of considering deployment-oriented metrics in the FS processes
dc.description.versionversión final
dc.identifier.citationGarcía-Merino, J.C., Tobarra-Abad, Ll., Robles-Gómez, A., Pastor-Vargas, R., Vidal-Balboa, P. (2025); Título: An Efficient Feature Selection Framework Using Genetic Algorithms; Publicación: 2025 International Conference on Computational Science and Computational Intelligence Proceedings.
dc.identifier.isbn1865-0929, 1865-0937
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30316
dc.language.isoen
dc.publisherSpringer
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentSistemas de Comunicación y Control
dc.relation.ispartofCommunications in Computer and Information Science
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject1203.17 Informática
dc.subject.keywordsArtificial Intelligence (AI)en
dc.subject.keywordsIntrusion Detection Systems (IDSs)en
dc.subject.keywordsfeature selectionen
dc.subject.keywordsInternet of Things (IoT)en
dc.subject.keywordsmodel efficiencyen
dc.titleAn Efficient Feature Selection Framework Using Genetic Algorithms for AI-Driven IDS in IoT Environmentsen
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.isAuthorOfPublication.latestForDiscoveryb584f8a3-eb01-4a43-9ed7-5075b74224ae
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