Detection of Wi-Fi transmitter transients using statistical method

Huang, Lianfen; Gao, Minghui; Zhao, Caidan; Wu, Xiongpeng

doi:10.1109/icspcc.2013.6664128

Cited by 17 publications

(14 citation statements)

References 12 publications

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“…In MCPD method, the difference between the statistic of samples is magnified, and the position that gives the maximum difference is determined as the start of transient [9]. The temporary vector in (14) is divided into sections:…”

Section: Mean Change Point Detection (Mcpd)mentioning

confidence: 99%

“…Phase Detection (PD) is the other method that exploits phase characteristics of signals for transient detection [8]. Mean Change Point Detection (MCPD) method is also presented in [9] to detect the transient starting point by calculating the maximum of the difference of statistic. In [10], step change detection scheme presented in [7] is modified by using Bayesian ramp change detector.…”

Section: Introductionmentioning

confidence: 99%

“…Then, different levels of noise (-3 to 25 dB) captured during data collection are added to the dataset. Next, the effect of additive noise on the transient start detection performance of the proposed and well-known methods [6][7][8][9] is experimentally demonstrated. Besides, the detection performances of the methods are evaluated by comparing their respective computational complexity and CPU elapsed time.…”

Section: Introductionmentioning

confidence: 99%

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Performance Assessment of Transient Signal Detection Methods and Superiority of Energy Criterion (EC) Method

2020

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Radio frequency fingerprinting (RFF) based on RF transients is one of the most effective techniques for improving wireless security. For an efficient RFF development, RF transients need to be accurately detected. However, the detection of the transient starting point remains a main challenge due to the channel noise. In the literature, several methods have been presented to detect the starting point of the transient signals. As an alternative to these methods, this study proposes a method that utilizes Energy Criterion (EC) technique for the first time. In order to test its performance, firstly, an extensive dataset consisting of Wi-Fi signals recorded under realistic Signal-to-Noise Ratio (SNR) conditions is created. Using the provided dataset, the proposed method as well as common transient detection methods are employed for transient start detection. Then, the effect of SNR on the performance of transient start detection is evaluated. Moreover, a performance comparison between the methods is provided based on their respective computational speed and complexity. The results prove the feasibility and efficiency of the proposed method to detect the transient starting point for RFF of Wi-Fi device identification. As to the knowledge of the authors, this study is the first report that comparatively assesses the transient detection methods by using extensive data under realistic noise conditions. INDEX TERMS Energy criterion, RF fingerprinting, transient signal detection, Wi-Fi signal. ISMAIL S. MOHAMED was born in Bani Waleed, Libya, in 1978. He received B.S. degree in communication engineering department from College of Electronic Technology, Bani Waleed city, in 2002. He was employed as an Engineer at the Communication Engineering Department where he was involved in preparing and supervising laboratory sessions. In 2008, he obtained M.S. degree in communication engineering from The Libyan Academy, Tripoli. He was a staff member at the Communication Engineering Department and was a teaching assistant at the department of Advanced Electronics in the Postgraduate studies. Currently, he is pursuing the Ph.D. degree at Atilim University, Turkey. His work is focused on Radio Frequency Fingerprinting.

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Section: Mean Change Point Detection (Mcpd)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance Assessment of Transient Signal Detection Methods and Superiority of Energy Criterion (EC) Method

2020

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“…Accurate transient detection is the key step to extract the features. Huang et al have proposed a method of mean change point detection [ 23 ] to analyze the transient based on phase detection [ 24 ]. Its idea is to magnify the difference between the statistic of samples before and after the section, and the position of the maximum difference is determined to be the start of transient.…”

Section: Methodsmentioning

confidence: 99%

Compressed Sensing Based Fingerprint Identification for Wireless Transmitters

Zhao

Huang

et al. 2014

The Scientific World Journal

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Most of the existing fingerprint identification techniques are unable to distinguish different wireless transmitters, whose emitted signals are highly attenuated, long-distance propagating, and of strong similarity to their transient waveforms. Therefore, this paper proposes a new method to identify different wireless transmitters based on compressed sensing. A data acquisition system is designed to capture the wireless transmitter signals. Complex analytical wavelet transform is used to obtain the envelope of the transient signal, and the corresponding features are extracted by using the compressed sensing theory. Feature selection utilizing minimum redundancy maximum relevance (mRMR) is employed to obtain the optimal feature subsets for identification. The results show that the proposed method is more efficient for the identification of wireless transmitters with similar transient waveforms.

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“…Consequently, the most important challenge in transient-based RFF can be attributed to detection of the transient starting point. In the literature, several methods have been proposed in order to efficiently detect the transient starting point [30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection

Mohamed¹,

Dalveren

Çatak

et al. 2022

Electronics

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In the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible threats to the wireless network. To overcome this challenge, the use of the transient region of the transmitted signals could be one of the best options. For an efficient transient-based RFF, it is also necessary to accurately and precisely estimate the transient region of the signal. Here, the most important difficulty can be attributed to the detection of the transient starting point. Thus, several methods have been developed to detect transient start in the literature. Among them, the energy criterion method based on the instantaneous amplitude characteristics (EC-a) was shown to be superior in a recent study. The study reported the performance of the EC-a method for a set of Wi-Fi signals captured from a particular Wi-Fi device brand. However, since the transient pattern varies according to the type of wireless device, the device diversity needs to be increased to achieve more reliable results. Therefore, this study is aimed at assessing the efficiency of the EC-a method across a large set of Wi-Fi signals captured from various Wi-Fi devices for the first time. To this end, Wi-Fi signals are first captured from smartphones of five brands, for a wide range of signal-to-noise ratio (SNR) values defined as low (−3 to 5 dB), medium (5 to 15 dB), and high (15 to 30 dB). Then, the performance of the EC-a method and well-known methods was comparatively assessed, and the efficiency of the EC-a method was verified in terms of detection accuracy.

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Detection of Wi-Fi transmitter transients using statistical method

Cited by 17 publications

References 12 publications

Performance Assessment of Transient Signal Detection Methods and Superiority of Energy Criterion (EC) Method

Performance Assessment of Transient Signal Detection Methods and Superiority of Energy Criterion (EC) Method

Compressed Sensing Based Fingerprint Identification for Wireless Transmitters

On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection

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