Santana-Cruz Rene Francisco, Moreno-Guzman Martin, Rojas-López César Enrique, Vázquez-Morán Ricardo, Vázquez-Medina Rubén
Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Santiago de Querétaro 76090, Mexico.
Universidad Tecnológica de San Juan del Río, San Juan del Río 76800, Mexico.
Sensors (Basel). 2024 Feb 24;24(5):1482. doi: 10.3390/s24051482.
The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by 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 RF identification techniques, which could be useful in forensic processes.
射频（RF）设备在当代社会的激增，尤其是在智能家居、物联网（IoT）小工具和智能手机领域，凸显了对强大识别方法以加强网络安全的迫切需求。本文基于将詹森-香农散度（JSD）应用于射频信号噪声的统计分布来识别蓝牙设备，深入探讨了射频指纹（RFF）领域。因此，通过详细的案例研究，探索了从不同设备以5 Gsps采集的蓝牙射频噪声。考虑一个噪声模型来提取独特、通用、永久、可收集且强大的统计射频指纹，以识别每个蓝牙设备。然后，将蓝牙设备提供的不同JSD噪声信号与所有设备的统计射频指纹进行对比，并声明成员资格分辨率。研究表明，这种基于射频指纹识别蓝牙设备的方法能够区分同一品牌和型号的设备，识别有效性达到99.5%。通过利用从设备发射的射频信号中的噪声提取的统计射频指纹，本研究不仅有助于推进基于无线通信的隐式设备认证系统领域的发展，还为射频识别技术的实际应用提供了有价值的见解，这在法医过程中可能会有用。
