Publicación: Assessing Feature Selection Techniques for AI-based IoT Network Intrusion Detection
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Fecha
2025-06
Autores
García Merino, José Carlos
Vidal Balboa, Pedro
Dionisio Rocha, André
Jardim Gonçalves, Ricardo
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Derechos de acceso
info:eu-repo/semantics/restrictedAccess
Licencia Creative Commons
http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
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Editor
Springer
Resumen
The widespread adoption of Internet of Things (IoT) technology in rural areas has led to qualitative leaps in fields such as agriculture, livestock farming, and transportation, giving rise to the concept of Smart Rural. However, Smart Rural IoT ecosystems are often vulnerable to cyberattacks. Although Artificial Intelligence (AI) based intrusion detection systems offer an effective solution to protect these environments, IoT devices are typically constrained in terms of memory and computation capabilities, making it essential to optimise the computational burden of AI models. This work explores different feature selection techniques to develop compact and fast Random Forest models for anomaly detection in IoT environments. The obtained results demonstrate that appropriate feature selection can reduce model size and inference time by at least 45% and 8%, respectively, without compromising predictive performance.
Descripción
This is the Accepted Manuscript of an article that will soon be published by Springer in "Lecture Notes in Networks and Systems", when the conference proceedings are published.
Este es el manuscrito aceptado de un artículo que Springer publicará próximamente en "Lecture Notes in Networks and Systems", cuando se publiquen las actas de la conferencia.
Categorías UNESCO
Palabras clave
Internet of Things (IoT), smart rural, cybersecurity, Artificial Intelligence (AI), Intrusion Detection System (IDS)
Citación
García-Merino, J.C., Tobarra-Abad, Ll., Robles-Gómez, A., Pastor-Vargas, R., Dionisio-Rocha, A., Jardim-Gonçalves, R. (2025); Título: Assessing Feature Selection Techniques for AI-based IoT Network Intrusion Detection; Publicación: DCAI 2025 - Lecture Notes in Networks and Systems. ISSN: 2367-3370; Páginas 1-10
Centro
E.T.S. de Ingeniería Informática
Departamento
Sistemas de Comunicación y Control