2026-07-23T06:30:14Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/119262026-07-21T00:05:19Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Martínez Fuentes, Virginia 985 600 0009-0007-7493-4059 Arroyo Puente, Ángel 741 600 0000-0002-1614-9075 Granados López, Diego 264 600 0000-0002-9046-7397 Herrero Cosío, Álvaro 283 600 0000-0002-2444-5384 2026-07-20T07:26:34Z 2026-07-20T07:26:34Z 2026-04 1615-5262 https://hdl.handle.net/10259/11926 10.1007/s10207-026-01235-z 1615-5270 The rapid expansion of Internet of Things (IoT) systems, found in environments such as smart homes, poses growing cybersecurity challenges. In response, research has examined the role of artificial intelligence, particularly machine learning, in enhancing IoT security. To support this effort, machine learning models have been developed and evaluated on benchmark datasets. However, preparing datasets for machine learning requires preprocessing techniques that are tailored to the specific characteristics of the data. In this context, exploratory data analysis provides insights into dataset structure and distribution, thereby supporting informed preprocessing decisions prior to modeling. Accordingly, this study introduces a reproducible five-step preprocessing framework for IoT cybersecurity datasets and demonstrates its application to the NF-ToN-IoT V1 dataset. The proposed framework is organized into two phases: an exploratory data analysis phase consisting of (1) dataset overview and identification of categorical and numerical features, (2) analysis of missing and zero values, (3) assessment of categorical feature distributions, and (4) assessment of numerical feature distributions; and a preprocessing phase consisting of (5) proportional stratified random downsampling to produce a reduced dataset that preserves the original class distribution. By establishing a systematic, data-driven framework, this study contributes to the preparation of structured datasets for attack detection in IoT environments, with potential applications in smart homes. This publication is part of the ‘Artificial Intelligence for Securing IoT Devices’ (AI4SECIoT) project, funded by the National Cybersecurity Institute (INCIBE) under grant number C032.23, and derived from a collaboration agreement signed between INCIBE and the University of Burgos. This initiative is carried out within the framework of the Recovery, Transformation, and Resilience Plan funds, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for solid, inclusive, and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade. application/pdf eng Springer International Journal of Information Security. 2026, V. 25, n. 2, art. 62 https://doi.org/10.1007/s10207-026-01235-z Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Cybersecurity IoT Dataset preprocessing Exploratory data analysis Downsampling Machine learning Internet de los objetos Internet of things Aprendizaje automático Machine learning Seguridad informática Computer security Toward AI-driven IoT cybersecurity: A preprocessing framework for benchmark datasets info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion International Journal of Information Security 25 2 THUMBNAIL Martinez-ijis_2026.pdf.jpg Martinez-ijis_2026.pdf.jpg IM Thumbnail image/jpeg 4512 https://riubu.ubu.es/bitstream/10259/11926/4/Martinez-ijis_2026.pdf.jpg 96d63f07da1219f2fc17bc3af2150881 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/11926/3/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/11926/2/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 2 ORIGINAL Martinez-ijis_2026.pdf Martinez-ijis_2026.pdf application/pdf 1099729 https://riubu.ubu.es/bitstream/10259/11926/1/Martinez-ijis_2026.pdf 383e27a7ee6305dd9a97c2f0b2f35f4e MD5 1 10259/11926 oai:riubu.ubu.es:10259/11926 2026-07-21 02:05:19.492 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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