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Fecha
2026-02-06
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
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Editorial
PeeJ
Citas
Resumen
The widespread adoption of Internet of Things (IoT) technology has driven significant advancements in fields such as agriculture, manufacturing, industry, and transportation. However, the highly interconnected and resource-constrained nature of IoT ecosystems makes them particularly vulnerable to cyberattacks. Although AI-based intrusion detection systems provide an effective protection, their deployment on IoT devices is hindered due to limited memory, processing power, and storage capacity. One strategy for addressing these limitations is dimensionality reduction, consisting of the removal of redundant or irrelevant features in order to reduce computational demands without compromising model accuracy. This work analyses the effectiveness of various dimensionality reduction approaches for the development of efficient and lightweight Random Forest models for anomaly detection in IoT environments. Among the
considered methods, Permutation Feature Importance consistently produced the most balanced models, reducing inference time, model size, and RAM usage, while slightly enhancing predictive performance. Furthermore, the feasibility of model deployment in real-world environments was assessed through experiments on a resource-constrained Raspberry Pi device.
Descripción
The registered version of this article, first published in “PeerJ Computer Science 12, 2026", is available online at the publisher's website: PeerJ, https://doi.org/10.7717/peerj-cs.3553
La versión registrada de este artículo, publicado por primera vez en “PeerJ Computer Science 12, 2026", está disponible en línea en el sitio web del editor: PeerJ, https://doi.org/10.7717/peerj-cs.3553
La versión registrada de este artículo, publicado por primera vez en “PeerJ Computer Science 12, 2026", está disponible en línea en el sitio web del editor: PeerJ, https://doi.org/10.7717/peerj-cs.3553
Categorías UNESCO
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Citación
García-Merino JC, Tobarra L, Robles-Gómez A, Pastor-Vargas R, Sarraipa J. 2026. A comparative study of dimensionality reduction techniques for intrusion detection in IoT networks. PeerJ Computer Science 12:e3553 https://doi.org/10.7717/peerj-cs.3553
Centro
E.T.S. de Ingenieros Industriales
Departamento
Sistemas de Comunicación y Control