Santana-Cruz, R.F.; Moreno-Guzman, M.; Rojas-López, C.E.; Vázquez-Morán, R.; Vázquez-Medina, R. Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence. Sensors2024, 24, 1482.
Santana-Cruz, R.F.; Moreno-Guzman, M.; Rojas-López, C.E.; Vázquez-Morán, R.; Vázquez-Medina, R. Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence. Sensors 2024, 24, 1482.
Santana-Cruz, R.F.; Moreno-Guzman, M.; Rojas-López, C.E.; Vázquez-Morán, R.; Vázquez-Medina, R. Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence. Sensors2024, 24, 1482.
Santana-Cruz, R.F.; Moreno-Guzman, M.; Rojas-López, C.E.; Vázquez-Morán, R.; Vázquez-Medina, R. Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence. Sensors 2024, 24, 1482.
Abstract
The proliferation of radio frequency devices in today's society, especially in smart homes, Internet of Things devices, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the field of radio frequency fingerprinting to propose a Bluetooth device identification method based on the application of the Jensen-Shannon divergence to the statistical distribution of noise in Bluetooth signals. A detailed case study is performed to investigate the Bluetooth radio frequency noise recorded at 5 Gsps from different devices is investigated to define a statistical radio frequency fingerprint for each Bluetooth device. A noise model is used to extract a unique, universal, persistent, recoverable, and robust statistical radio frequency fingerprint that identifies each Bluetooth device. Then, using the Jensen-Shannon divergence, different noise signals provided by each Bluetooth device are compared with the statistical radio frequency fingerprint of all devices and a membership resolution is declared. The study shows that the proposed method can discriminate between devices of the same brand and model, achieving an identification effectiveness of 99.5 \%. By leveraging the statistical radio frequency fingerprint of Bluetooth devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of radio frequency identification techniques that could be useful in forensic processes.
Keywords
Identification systems; Radio Frequency Fingerprints (RFF); IoT device identification; Cybersecurity; Wireless Communication
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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