WiMAX mobile subscriber verification using Gabor-based RF-DNA fingerprints

Reising, Donald R.; Temple, Michael A.

doi:10.1109/icc.2012.6364039

Cited by 22 publications

(24 citation statements)

References 16 publications

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“…And the future work could also focus on the method of extracting fingerprint and the classifier chosen. For example, recent researches took short-time Fourier transforms [22] and discrete Gabor transform [10] to generate RF-DNA fingerprint. Besides, some neural network model [17,20] could also be used as classifiers.…”

Section: Discussionmentioning

confidence: 99%

“…Firstly, research [2,6] enabled both identification and verification device issues and extended the process from the three-class to general N-class problems. By setting a priori distribution of multivariate Gaussian distribution, posteriori probability could be calculated [2,10] to achieve authentication simulation. Previous studies showed the impact on the number of dimensions [7,11] and the number of subregion [8] on classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Generalized relevance learning vector quantization-improved [12,18,19] is a supervised machine learning algorithm based on MDA, which shows better performance. As for the different feature extraction methods, fast Fourier transform [20,21], the short-time Fourier transform [22], and discrete Gabor transform [10] could also be used to extract features. Previous research had compared their performance, including on time domain, wavelet domain [23], and spectral domain [24] features.…”

Section: Introductionmentioning

confidence: 99%

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Identification and authentication for wireless transmission security based on RF-DNA fingerprint

Wang

Zhang

et al. 2019

J Wireless Com Network

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For wireless transmission, radio-frequency device anti-cloning has become a major security issue. Radio-frequency distinct native attribute (RF-DNA) fingerprint is a developing technology to find the difference among RF devices and identify them. Comparing with previous research, (1) this paper proposed that mean (μ) feature should be added into RF-DNA fingerprint. Thus, totally four statistics (mean, standard deviation, skewness, and kurtosis) were calculated on instantaneous amplitude, phase, and frequency generated by Hilbert transform. (2) We first proposed using the logistic regression (LR) and support vector machine (SVM) to recognize such extracted fingerprint at different signal-to-noise ratio (SNR) environment. We compared their performance with traditional multiple discriminant analysis (MDA). (3) In addition, this paper also proposed to extract three sub-features (amplitude, phase, and frequency) separately to recognize extracted fingerprint under MDA. In order to make our results more universal, additive white Gaussian noise was adopted to simulate the real environment. The results show that (1) mean feature conducts an improvement in the classification accuracy, especially in low SNR environment. (2) MDA and SVM could successfully identify these RF devices, and the classification accuracy could reach 94%. Although the classification accuracy of LR is 89.2%, it could get the probability of each class. After adding a different noise, the recognition accuracy is more than 80% when SNR ≥ 5 dB using MDA or SVM. (3) Frequency feature has more discriminant information. Phase and amplitude play an auxiliary but also pivotal role in classification recognition.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification and authentication for wireless transmission security based on RF-DNA fingerprint

Wang

Zhang

et al. 2019

J Wireless Com Network

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“…An approach to improve the security of data communication through a vulnerable network channel consists in defining RF Distinct Native Attributes (RF-DNA) features of hardware devices (PHY layers) [6], which are inherently unique for a given device [7]. In this paper, these RF-DNA features are analyzed and processed for the discrimination and rejection of spoofing devices.…”

Section: Introductionmentioning

confidence: 99%

Rogue device discrimination in ZigBee networks using wavelet transform and autoencoders

2020

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In modern wireless systems such as ZigBee, sensitive information which is produced by the network is transmitted through different wired or wireless nodes. Providing the requisites of communication between diverse communication system types, such as mobiles, laptops, and desktop computers, does increase the risk of being attacked by outside nodes. Malicious (or unintentional) threats, such as trying to obtain unauthorized accessibility to the network, increase the requirements of data security against the rogue devices trying to tamper with the identity of authorized devices. In such manner, focusing on Radio Frequency Distinct Native Attributes (RF-DNA) of features extracted from physical layer responses (referred to as preambles) of ZigBee devices, a dataset of distinguishable features of all devices can be produced which can be exploited for the detection and rejection of spoofing/rogue devices. Through this procedure, distinction of devices manufactured by the different/same producer(s) can be realized resulting in an improvement of classification system accuracy. The two most challenging problems in initiating RF-DNA are (1) the mechanism of features extraction in the generation of a dataset in the most effective way for model classification and (2) the design of an efficient model for device discrimination of spoofing/rogue devices. In this paper, we analyze the physical layer features of ZigBee devices and present methods based on deep learning algorithms to achieve high classification accuracy, based on wavelet decomposition and on the autoencoder representation of the original dataset.

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“…On one hand, the statistical characteristics of the original RF signal, such as the high order spectrum, have been used as the features to identify different emitters [7,8]. On the other hand, the Time-Frequency Analysis (TFA) methods [9,10], Wavelet Transform (WT) [11,12], and Hilbert-Huang Transform (HHT) [13,14] are successively applied to extract the transform domain characteristics from the received RF signals. However, these methods have little knowledge of impairments inside the individual emitter, and the performance can be easily affected by the wireless channels.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Antenna Emitters Identification Based on a Memoryless Power Amplifier Model

2019

Sensors

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Power amplifier (PA) nonlinearity is typically unique at the radio frequency (RF) front-end for particular emitters. It can play a crucial role in the application of specific emitter identification (SEI). In this paper, under the Multi-Input Multi-Output (MIMO) multipath communication scenario, two data-aided approaches are proposed to identify multi-antenna emitters using PA nonlinearity. Built upon a memoryless polynomial model, the first approach formulates a linear least square (LLS) problem and presents the closed-form solution of nonlinear coefficients in a MIMO system by means of singular value decomposition (SVD) operation. Another alternative approach estimates nonlinear coefficients of each individual PA through nonlinear least square (NLS) solved by the regularized Gauss–Newton iterative scheme. Moreover, there are some practical discussions of our proposed approaches about the mismatch of the order of PA model and the rank-deficient condition. Finally, the average misclassification rate is derived based on the minimum error probability (MEP) criterion, and the proposed approaches are validated to be effective through extensively numerical simulations.

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WiMAX mobile subscriber verification using Gabor-based RF-DNA fingerprints

Cited by 22 publications

References 16 publications

Identification and authentication for wireless transmission security based on RF-DNA fingerprint

Identification and authentication for wireless transmission security based on RF-DNA fingerprint

Rogue device discrimination in ZigBee networks using wavelet transform and autoencoders

Multiple-Antenna Emitters Identification Based on a Memoryless Power Amplifier Model

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