Radio Frequency Fingerprint Extraction Based on Multidimension Permutation Entropy

Abstract

Radio frequency fingerprint (RF fingerprint) extraction is a technology that can identify the unique radio transmitter at the physical level, using only external feature measurements to match the feature library. RF fingerprint is the reflection of differences between hardware components of transmitters, and it contains rich nonlinear characteristics of internal components within transmitter. RF fingerprint technique has been widely applied to enhance the security of radio frequency communication. In this paper, we propose a new RF fingerprint method based on multidimension permutation entropy. We analyze the generation mechanism of RF fingerprint according to physical structure of radio transmitter. A signal acquisition system is designed to capture the signals to evaluate our method, where signals are generated from the same three Anykey AKDS700 radios. The proposed method can achieve higher classification accuracy than that of the other two steady-state methods, and its performance under different SNR is evaluated from experimental data. The results demonstrate the effectiveness of the proposal.

An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

“…The characteristics of entropy are frequently used features in RF fingerprint. For instance, the authors of [23] proposed recognition method of a new fingerprint established on multi-dimensional permutation entropy. According to this experiment, the transceiver distance is fixed to 10 meters, thus the signal can be propagated in the channel of short-wave line of sight.…”

Identifying GNSS Signals Based on Their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator

“…Research has also been done in the field of radio identifications, for example in References [11][12][13]. The authors of Reference [11] study the hardware impairments and investigates the Convolutional Neural Networks (CNN).…”

“…The authors of Reference [11] study the hardware impairments and investigates the Convolutional Neural Networks (CNN). The authors of Reference [12] provide a tutorial of device fingerprinting in a wireless device context, while Reference [13] introduces Permutation Entropy (PE) methods to identify devices by evaluating the level of chaos in the received signals.…”

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.…”