LATIDIA · Ciberseguridad

Hacia la detección ligera de intrusiones en IoT: optimización de funciones y aprendizaje automático explicable para entornos con recursos limitados

arXiv: 2611.10707v1Tipo de anuncio: nuevo Resumen: Los sistemas de detección de intrusos (IDS) basados en el aprendizaje automático pueden mejorar la seguridad de IoT, pero a menudo dependen de grandes conjuntos de características que aumentan la sobrecarga computacional y limitan el deplo

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arXiv:2611.10707v1 Announce Type: new Abstract: Machine learning-based intrusion detection systems (IDSs) can improve IoT security but often rely on large feature sets that increase computational overhead and limit deployment on resource-constrained devices. This paper presents a resource-efficient and explainable feature optimization framework for lightweight IoT intrusion detection using the CICIoT2023 dataset. A Random Forest model was used to rank feature importance and identify compact feature subsets. Experimental results show that a three-feature model achieved an MCC of 0.9622 while reducing the feature space by 92.1%, whereas a six feature model achieved an MCC of 0.9675 with an 84.2% reduction. Furthermore, a fifteen-feature model achieved an MCC of 0.9744, preserving 99.97%