Improvement of transmitter identification system for low SNR transients

Tekbaş, Önder Haluk; Üreten, O.; Serinken, N.

doi:10.1049/el:20040160

Cited by 49 publications

(14 citation statements)

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“…8. In [26], Tekbas et al have discussed the method that can improve the identification performance at low SNR levels. Firstly, a limited number of classifiers are trained by the training sets with different SNRs.…”

Section: Resultsmentioning

confidence: 99%

Specific Emitter Identification Based on Transient Energy Trajectory

Yuan¹,

Huang²,

Sha³

2013

PIER C

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Abstract-Specific emitter identification (SEI) is the technique which identifies the individual emitter based on the RF fingerprint of signal. Most existing SEI techniques based on the transient RF fingerprint are sensitive to noise and need different variables for transient detection and RF fingerprint extraction. This paper proposes a novel SEI technique for the common digital modulation signals, which is robust to Gaussian noise and can avoid the problem that different variables are needed for transient detection and RF fingerprint extraction. This makes the technique more practical. The technique works based on the signal's energy trajectory acquired by the fourth order cumulants. A relative smoothness measure detector is used to detect the starting point and endpoint of the transient signal. The polynomial fitting coefficients of the energy trajectory and transient duration form the RF fingerprint. The principal component analysis (PCA) technique is used to reduce the feature vector's dimension, and a support vector machine (SVM) classifier is used for classification. The signals captured from eight mobile phones are used to test the performance of the technique, and the experimental results demonstrate that it has good performance even at low SNR levels.

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Section: Resultsmentioning

confidence: 99%

Specific Emitter Identification Based on Transient Energy Trajectory

Yuan¹,

Huang²,

Sha³

2013

PIER C

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“…This is more complicated during noisy RF channel conditions or over larger transmission distances. Tekbas et al [2004] proposed methods to extract features for RF fingerprints with orthogonal dimensions and give a method to reduce the size of the training template database and subsequent classification feature set. They showed that a matched filter to the channel provides the best basis for this orthogonalization process.…”

Section: Related Workmentioning

confidence: 99%

Wireless Fingerprints Inside a Wireless Sensor Network

Knox

Kunz

2015

ACM Trans. Sen. Netw.

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We discriminate between different SiLabs IEEE 802.15.4 2.4GHz RF sources using the Ettus Labs USRP1 Software-Defined Radio. The wireless fingerprinting method implemented on the USRP1 device exploits differences in the phase attributes of demodulated data samples. The method does not require the use of expensive spectrum analyzer equipment and the associated high sampling and processing rates with such equipment. Instead, data sample inputs are used, sampled at a rate of 4MHz. This makes implementation using real Wireless Sensor Network nodes feasible and allows wireless fingerprints to be gathered inside each node in a network. This is important since wireless fingerprints degrade over distance, making distributed implementations more attractive. With our method, the USRP1 classifies accurately over a wide range of network conditions, including time and transmission distance. Performance is also stable for different receiving devices. We achieve average classification accuracies of 99.6% at short range, 95.3% at medium range, and 81.9% at long range when classifying a limited sample of five devices from the same manufacturer.

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“…So far, it has been shown that a number of devices (or classes of devices) can be identified using physicallayer identification. These include analog VHF transmitters [ Hippenstiel and Payal 1996;Ureten and Serinken 2007a;Tekbas et al 2004aTekbas et al , 2004b, IEEE 802.11 transceivers [Hall et al 2004[Hall et al , 2005Hall 2006;Ureten and Serinken 2007b;Brik et al 2008;Suski et al 2008a;Klein et al 2009], IEEE 802.15.4 transceivers , Bluetooth transceivers ], UHF sensor nodes [Rasmussen and Capkun 2007], HF RFID Romero et al 2010] and UHF RFID [Periaswamy et al 2010a;Zanetti et al 2010] transponders. All these devices are composed of antennas, analog frontends, and logic units, but have different levels of complexity, for example, IEEE 802.11 transceivers (Figure 2(b)) are complex whereas RFID transponders are relatively simple (Figure 2(a)).…”

Section: Device Under Identificationmentioning

confidence: 99%

“…The experimental results showed that the system needed to be trained over a wide temperature range and the operational supply-voltage levels in order to achieve low classification error rates of 5%. Classification accuracy of low-SNR transients could be improved by estimating the SNR and modifying its level in the training phase [Tekbas et al 2004b]. Transient-based approaches were also investigated in modern wireless local and personal area networks (WLAN/WPAN), primarily for intrusion detection and access control.…”

Section: Transient-based Approachesmentioning

confidence: 99%

On physical-layer identification of wireless devices

2012

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Physical-layer device identification aims at identifying wireless devices during radio communication by exploiting unique characteristics of their analog (radio) circuitry. This work systematizes the existing knowledge on this topic in order to enable a better understanding of device identification, its implications on the analysis and design of security solutions in wireless networks and possible applications. We therefore present a systematic review of physical-layer identification systems and provide a summary of current state-of-the-art techniques. We further present a classification of attacks and discuss the feasibility, limitations, and implications in selected applications. We also highlight issues that are still open and need to be addressed in future work.

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Improvement of transmitter identification system for low SNR transients

Cited by 49 publications

References 1 publication

Specific Emitter Identification Based on Transient Energy Trajectory

Specific Emitter Identification Based on Transient Energy Trajectory

Wireless Fingerprints Inside a Wireless Sensor Network

On physical-layer identification of wireless devices

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