An Overview of Radio Frequency Fingerprinting for Low-End Devices

Abstract

RF fingerprinting is proposed as a means of providing an additional layer of security for wireless devices. A masquerading or impersonation attacks can be prevented by establishing the identity of wireless transmitter using unique transmitter RF fingerprint. Unique RF fingerprints are attributable to the analog components (digital-to-analog converters, band-pass filters, frequency mixers and power amplifiers) present in the RF front ends of transmitters. Most of the previous researches have reported promising results with an accuracy of up to 99% using high-end receivers (e.g. Giga-sampling rate oscilloscopes, spectrum and vector signal analysers) to validate the proposed techniques. However, practical implementation of RF fingerprinting would require validation with low-end (low-cost) devices that also suffers from impairments due to the presence of analog components in the front end of its receiver. This articles provides the analysis and implementation of RF fingerprinting using low-cost receivers and challenges associated with it.

Industrial IoT cross‐layer forensic investigation

“…While the cross‐device uniqueness of electronic device fingerprints may not be totally on par with cross‐human fingerprint uniqueness, results such as provided in Deng et al (), Hall et al (), Huang and Zheng (); Lopez Jr. et al, ; Mirowski et al, ; Rehman et al, ; Reising et al, ; Rondeau et al, ; Suski et al, ; Talbot et al, ; Zhuo et al, ) routinely demonstrate near 100% discrimination for selected scenarios and have been sufficiently promising to sustain progressive RDD over the past 10 years. Collectively, these and other related RFF works have addressed nearly all common communication signaling schemes, including Bluetooth (Hall et al, ), automation (Lopez Jr. et al, ; Talbot et al, ) and ZigBee (Rondeau et al, ) Personal Area Networks (PANs); WiFi (Huang & Zheng, ; Rehman et al, ; Suski et al, ; Zhuo et al, ) Wireless Local Area Network (WLANs), and WiMAX (Deng et al, ; Reising et al, ) Wide Area Networks (WANs), to name a few. For the references provided, the unique RFF features have been reliably extracted from various signal domains, including (a) time (Deng et al, ; Hall et al, ; Lopez Jr. et al, ; Rehman et al, ; Suski et al, ), (b) frequency (Lopez Jr. et al, ; Suski et al, ; Talbot et al, ), (c) joint time–frequency (Reising et al, ; Zhuo et al, ), and (d) constellation (Huang & Zheng, ; Rondeau et al, ).…”

“…Collectively, these and other related RFF works have addressed nearly all common communication signaling schemes, including Bluetooth (Hall et al, ), automation (Lopez Jr. et al, ; Talbot et al, ) and ZigBee (Rondeau et al, ) Personal Area Networks (PANs); WiFi (Huang & Zheng, ; Rehman et al, ; Suski et al, ; Zhuo et al, ) Wireless Local Area Network (WLANs), and WiMAX (Deng et al, ; Reising et al, ) Wide Area Networks (WANs), to name a few. For the references provided, the unique RFF features have been reliably extracted from various signal domains, including (a) time (Deng et al, ; Hall et al, ; Lopez Jr. et al, ; Rehman et al, ; Suski et al, ), (b) frequency (Lopez Jr. et al, ; Suski et al, ; Talbot et al, ), (c) joint time–frequency (Reising et al, ; Zhuo et al, ), and (d) constellation (Huang & Zheng, ; Rondeau et al, ). A majority of RFF works available for forensic consideration are based on burst‐type communications, which when used for committing a cyberattack or electronic crime, may leave behind (1) only a single fingerprint—this may occur for a simple attack against a ZigBee control element that is designed to respond to a single command burst or (b) 10s–1000s of fingerprints—this may occur for a progressive multinode WiFi network attack with the actual number of fingerprints “left behind” by the perpetrator(s) depending on the extent and duration of the attack.…”

“…Thus, the IIoT PHY domain is rich in forensic information that may be exploited for a postattack investigation. Outside of forensic applications there has been considerable RDD activity in single layer PHY-based RFF methods developed to support network defense of both wired and wireless communications (Deng, Huang, Wang, & Huang, 2017;Hall, Barbeau, & Kranakis, 2003;Huang & Zheng, 2012;Lopez Jr., Liefer, Temple, & Busho, 2018;Mirowski, Milioris, Whiting, & Ho, 2014;Rehman, Alam, & Ardekani, 2014;Reising, Temple, & Jackson, 2015;Rondeau, Betances, & Temple, 2018;Suski, Temple, Mendenhall, & Mills, 2008;Talbot, Temple, Carbino, & Betances, 2018;Zhuo, Huang, & Chen, 2017). These works have predominantly addressed the discrimination of various hardware components (e.g., the gateway devices shown in Figure 4) used to establish communications.…”

Radio Frequency Fingerprint Identification Based on Metric Learning

“…Feature extraction determines the quality of RFF and directly affects classification accuracy. Many studies have explored the characteristics of different electronic components to extract effective RFF features-for example, in-phase and quadrature offset (Brik et al, 2008), phase offset (Nguyen et al, 2011), carrier frequency offset (Wheeler et al, 2017), differential constellation trace figure , and signal spectrum (Rehman et al, 2014). Recently, Wang et al (2016) built a theoretical model for the entire wireless communication link to analyze the effectiveness of different RFF features.…”