Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence

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 fi...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-02, Vol.24 (5), p.1482
Main Authors: Santana-Cruz, Rene Francisco, Moreno-Guzman, Martin, Rojas-López, César Enrique, Vázquez-Morán, Ricardo, Vázquez-Medina, Rubén
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Bluetooth technology
Case studies
Classification
cybersecurity
Data encryption chips
Identification
identification systems
Internet
IoT device identification
Machine learning
Measurement techniques
Methods
Neural networks
radio frequency fingerprints (RFF)
Receivers & amplifiers
Safety and security measures
Signal processing
Smart phones
Transmitters
wireless communication
Wireless communications
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24051482