Design of a Hybrid RF Fingerprint Extraction and Device Classification Scheme

Peng, Linning; Hu, Aiqun; Zhang, Junqing; Jiang, Yu; Yu, Jongmin; Yan, Yan

doi:10.1109/jiot.2018.2838071

Cited by 222 publications

(92 citation statements)

References 45 publications

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“…DCTF does not require any prior synchronization information about the receiver, which makes it a kind of stable transmitter feature that can be extracted at the receiver. Besides, The research in [37] verified that DCTF is a stable RF fingerprint through experimental measurements for 18 months, which will not vary significantly over time. Moreover, this method is also effective for signals modulated in other ways.…”

Section: Dctf-based Rf Fingerprint Extractionmentioning

confidence: 64%

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Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

Chen¹,

Peng²,

Hu³

et al. 2020

Preprint

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With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure (DCTF) generation process is initially employed to transform RF fingerprint features from time-domain waveforms to 2-dimensional (2D) figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A USRP receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.

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Section: Dctf-based Rf Fingerprint Extractionmentioning

confidence: 64%

“…• The existing applications of DCTF [19,30,37,38] are for the classification of known devices, while this paper solves the problem of rogue device identification. This problem is more challenging since we cannot obtain any prior information about rogue devices.…”

Section: Contributionsmentioning

confidence: 99%

Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

Chen¹,

Peng²,

Hu³

et al. 2020

Preprint

Self Cite

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show abstract

“…The classification error rate is as low as 0.048 in the LOS scenario, and 0.1105, even when a different receiver is used for classification, 18 months after the training. In 2019, Wang et al selected different characteristics of RF fingerprints and compareed the identification accuracy of Zigbee devices with five classification algorithms [30]. The experimental research shows that the highest identification accuracy reached approximately 100% by using multi-features of frequency offset, IQ offset, and circle offset based on the neural network algorithm under a high SNR.…”

Section: Rf Fingerprint Feature Extractionmentioning

confidence: 99%

“…Previous work has focused on using RFF technology to authenticate mobile devices [29][30][31][32][33][34][35]. This authentication method requires the device to be authenticated to provide a claimed identity in advance.…”

Section: Rf Fingerprint Authenticationmentioning

confidence: 99%

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

Tian

Lin

Guo

et al. 2020

Sensors

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With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

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“…Because the inherent characteristics and specifications of different IoT devices (e. g., radio frequency) are not completely consistent, RF fingerprinting technology detects and identifies different devices by extracting subtle differences. In addition, the process of RF fingerprinting recognition usually includes two steps: training and classification [12]- [14], which are shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Deep Complex-valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

Gu¹,

Qing²,

Wang³

et al. 2020

Preprint

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Drones-aided ubiquitous applications play more and more important roles in our daily life. Accurate recognition of drones is required in aviation management due to their potential risks and even disasters. Radio frequency (RF) fingerprintingbased recognition technology based on deep learning is considered as one of the effective approaches to extract hidden abstract features from RF data of drones. Existing deep learning-based methods are either a high computational burden or low accuracy. In this paper, we propose a deep complex-valued convolutional neural network (DC-CNN) method based on RF fingerprinting for recognizing different drones. Compared with existing recognition methods, the DC-CNN method has the advantages of high recognition accuracy, fast running time and small network complexity. Nine algorithm models and two datasets are used to represent the superior performance of our system. Experimental results show that our proposed DC-CNN can achieve recognition accuracy of 99.5% and 74.1% respectively on 4 and 8 classes of RF drone datasets.

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Design of a Hybrid RF Fingerprint Extraction and Device Classification Scheme

Cited by 222 publications

References 45 publications

Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

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

Deep Complex-valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

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