Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine

Rene Francisco Santana-Cruz, Martin Moreno (), Daniel Aguilar-Torres, Román Arturo Valverde-Domínguez and Rubén Vázquez-Medina ()
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Rene Francisco Santana-Cruz: Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Unidad Querétaro, Querétaro 76090, Mexico
Martin Moreno: Universidad Tecnológica de San Juan del Río, San Juan del Río, Querétaro 76800, Mexico
Daniel Aguilar-Torres: Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Unidad Querétaro, Querétaro 76090, Mexico
Román Arturo Valverde-Domínguez: Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Energía y Movilidad, Mexico City 07738, Mexico
Rubén Vázquez-Medina: Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Unidad Querétaro, Querétaro 76090, Mexico

Future Internet, 2025, vol. 17, issue 6, 1-25

Abstract: Device identification based on radio frequency fingerprinting is widely used to improve the security of Internet of Things systems. However, noise and acquisition inconsistencies in raw radio frequency signals can affect the effectiveness of classification, identification and authentication algorithms used to distinguish Bluetooth devices. This study investigates how the RF signal preprocessing techniques affect the performance of a support vector machine classifier based on radio frequency fingerprinting. Four options derived from an RF signal preprocessing technique are evaluated, each of which is applied to the raw radio frequency signals in an attempt to improve the consistency between signals emitted by the same Bluetooth device. Experiments conducted on raw Bluetooth signals from twentyfour smartphone radios from two public databases of RF signals show that selecting an appropriate RF signal preprocessing approach can significantly improve the effectiveness of a support vector machine classifier-based algorithm used to discriminate Bluetooth devices.

Keywords: RF device identification; IoT Authentication; RF fingerprints; Bluetooth devices; support vector machine (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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