Abstract

Radio frequency fingerprinting (RFF) is one of the communication network’s security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (~4% higher) at lower SNR levels (−5–5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices.

Highlights

  • As a physical-layer security method, radio frequency fingerprinting (RFF) offers reasonable means to ensure protection against attacks from complex threats in wireless networks

  • The classification performance results show that better performance is achieved (4% higher) when higher order statistical (HOS) features are extracted from the band-limited modes directly

  • HOS features extracted from reconstructed transients, classification accuracy is 67.5%, andclassification

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Summary

Introduction

As a physical-layer security method, radio frequency fingerprinting (RFF) offers reasonable means to ensure protection against attacks from complex threats in wireless networks. RFF utilizes inherently unique-distinctive features, or so-called “RF fingerprints”, of physical waveforms transmitted from wireless devices to classify authorized users, and identify threats [4]. These features are extracted from the transient or steady-state regions of transmitted signals. Among these techniques, HHT provides an accurate way to extract subtle features by decomposing the transient signal, both in the time and frequency domain [12]. VMD is based on the simultaneous decomposition of modes non-recursively, both in the temporal and spectral domain It is computationally simple, and does not suffer from any mode mixing problem. On the other hand, regarding the performance of VMD with transient signals, VMD has been successfully demonstrated with Bluetooth (BT) devices [18]

Related Works
Contributions
Variational
Transient
Classification
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