RT info:eu-repo/semantics/article
T1 Toward AI-driven IoT cybersecurity: A preprocessing framework for benchmark datasets
A1 Martínez Fuentes, Virginia
A1 Arroyo Puente, Ángel
A1 Granados López, Diego
A1 Herrero Cosío, Álvaro
K1 Cybersecurity
K1 IoT
K1 Dataset preprocessing
K1 Exploratory data analysis
K1 Downsampling
K1 Machine learning
K1 Internet de los objetos
K1 Internet of things
K1 Aprendizaje automático
K1 Machine learning
K1 Seguridad informática
K1 Computer security
AB 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.
PB Springer
SN 1615-5262
YR 2026
FD 2026-04
LK https://hdl.handle.net/10259/11926
UL https://hdl.handle.net/10259/11926
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 23-jul-2026