Radio frequency fingerprinting identification for Zigbee via lightweight CNN

Qing, Guangwei; Wang, Huifang; Zhang, Tingping

doi:10.1016/j.phycom.2020.101250

Cited by 25 publications

(12 citation statements)

References 10 publications

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“…Since then, several authors have explored the RF fingerprinting method for IoT using feature learning approaches [6,30,31]. Particularly, many works address the complexity problem in IoT context using a lightweight method [32][33][34]. For example, in [32], the authors present an lightweight procedure based on mobile edge computing (MEC) in IoT context.…”

Section: Feature-learning For Rf Fingerprintingmentioning

confidence: 99%

“…Compared to their results, the proposed classifier 1 and classifier 2 show better performances. In [34], the authors proposed a lightweight CNN architecture for RF fingerprinting of four Zigbee devices. Their architecture showed less noise robustness than classifier 1 but showed better noise robustness than classifier 2 and 3 for low SNR (0-10 dB).…”

Section: Real-world Performance Evaluationmentioning

confidence: 99%

“…For our part, the model trained in Section 4.2 was composed of 2282 complex parameters. However, our method is more scalable than the approach presented in [33] because the number of features, K, does not depend on class number C. Moreover, several authors proposed lightweight CNN architecture for RF fingerprinting in IoT context [33,34]. Although their architectures were smaller than classic CNN architectures, the number of trainable parameters are still around 200,000.…”

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confidence: 98%

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RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context

Morge-Rollet

Roy

Jeune

et al. 2022

Sensors

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In IoT networks, authentication of nodes is primordial and RF fingerprinting is one of the candidates as a non-cryptographic method. RF fingerprinting is a physical-layer security method consisting of authenticated wireless devices using their components’ impairments. In this paper, we propose the RF eigenfingerprints method, inspired by face recognition works called eigenfaces. Our method automatically learns important features using singular value decomposition (SVD), selects important ones using Ljung–Box test, and performs authentication based on a statistical model. We also propose simulation, real-world experiment, and FPGA implementation to highlight the performance of the method. Particularly, we propose a novel RF fingerprinting impairments model for simulation. The end of the paper is dedicated to a discussion about good properties of RF fingerprinting in IoT context, giving our method as an example. Indeed, RF eigenfingerprint has interesting properties such as good scalability, low complexity, and high explainability, making it a good candidate for implementation in IoT context.

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Section: Feature-learning For Rf Fingerprintingmentioning

confidence: 99%

Section: Real-world Performance Evaluationmentioning

confidence: 99%

mentioning

confidence: 98%

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RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context

Morge-Rollet

Roy

Jeune

et al. 2022

Sensors

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“…Frequency Fingerprint Identification(RFFI) [88,120,104,160] is more secure and reliable than using IP for wireless device authentication.…”

Section: Automated User Authenticationmentioning

confidence: 99%

Automation and Orchestration of Zero Trust Architecture:Potential Solutions and Challenges

Cao¹,

Pokhrel²,

Zhu³

et al. 2022

Preprint

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<p>Zero Trust Architecture (ZTA) is a paradigm shift in how we protect data, stay connected, and access resources. ZTA is non-perimeter based defense, which has been emerging as a promising revolution in the cybersecurity field. It can be used to continuously maintain security by safeguarding against attacks both from inside and outside of the network system. However, automation and orchestration ZTA, an essential direction towards its seamless deployments over real-word networks, have been poorly understood in literature. In this paper, we first identify the bottlenecks, discuss the background of ZTA and compare it with traditional perimeter-based security architectures. More importantly, we present a comprehensive direction towards the automation and orchestration of ZTA by employing AI techniques. In other words, the key potential and roles of celebrates AI techniques for the automation and orchestration of ZTA are demonstrated for further exploration. Overall, in this review paper, we develop a foundational view on the challenges and potential enablers for the automation and orchestration of ZTA.</p>

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“…For protection purposes, a promising physical-layer security method known as radio frequency fingerprinting (RFF) has been used on various wireless technologies. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] The RFF method secures the identity of wireless communication devices by exploiting the distinctive or unique features, which are referred to as fingerprints, extracted from radio waves emitted by wireless transceivers. The feature distinctiveness is linked with the physical characteristics of wireless transceivers, since the transceivers have different physical characteristics due to the imperfections of analog components in their circuitry.…”

Section: Introductionmentioning

confidence: 99%

A study on the performance evaluation of wavelet decomposition in transient‐based radio frequency fingerprinting of Bluetooth devices

Almashaqbeh

Dalveren

Kara

2022

Micro & Optical Tech Letters

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Radio frequency fingerprinting (RFF) is used as a physical-layer security method to provide security in wireless networks. Basically, it exploits the distinctive features (fingerprints) extracted from the physical waveforms emitted from radio devices in the network. One of the major challenges in RFF is to create robust features forming the fingerprints of radio devices. Here, dual-tree complex wavelet transform (DT-CWT) provides an accurate way of extracting those robust features. However, its performance on the RFF of Bluetooth transients which fall into narrowband signaling has not been reported yet. Therefore, this study examines the performance of DT-CWT features on the use of transient-based RFF of Bluetooth devices. Initially, experimentally collected Bluetooth transients from different smartphones are decomposed by DT-CWT. Then, the characteristics and statistics of the wavelet domain signal are exploited to create robust features. Next, the support vector machine (SVM) is used to classify the smartphones. The classification accuracy is demonstrated by varying channel signal-to-noise ratio (SNR) and the size of transient duration. Results show that reasonable accuracy can be achieved (lower bound of 88%) even with short transient duration (1024 samples) at low SNRs (0-5 dB).

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Radio frequency fingerprinting identification for Zigbee via lightweight CNN

Cited by 25 publications

References 10 publications

RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context

RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context

Automation and Orchestration of Zero Trust Architecture:Potential Solutions and Challenges

A study on the performance evaluation of wavelet decomposition in transient‐based radio frequency fingerprinting of Bluetooth devices

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