Measuring wireless fingerprints inside a wireless sensor network

Knox, D. Broughton

doi:10.22215/etd/2013-09502

Cited by 2 publications

(3 citation statements)

References 35 publications

(41 reference statements)

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“…In other work [Knox 2013], we use statistical analysis techniques to study the independence of classification errors made by different receivers. We show that the classification errors made by different receivers for the same message are statistically independent.…”

Section: Experimental Results: Performance Variationmentioning

confidence: 99%

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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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Section: Experimental Results: Performance Variationmentioning

confidence: 99%

“…The basic code structure is shown in Figure 6. A detailed analysis of the processing overhead and processing complexity of the code is given in Knox [2013].…”

Section: Experimental Platform (Ettus Research Inc Usrp1)mentioning

confidence: 99%

Wireless Fingerprints Inside a Wireless Sensor Network

Knox

Kunz

2015

ACM Trans. Sen. Netw.

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“…Hall et al 2 used frequency and amplitude information to create hardware fingerprints from ten different transient features to identify WLAN.Suski and other scholars extracted the power spectral density of the preamble signal transmitted by the wireless device terminal in the literature 3 . Afolabi et al used a simulation model and extracted six features from the preprocessed transient signal by using amplitude, phase and frequency curves to form a hardware fingerprint 4 .Knox et al extracted the phase information in the baseband signal transmitted by the wireless device terminal as the hardware fingerprint feature to identify the transmitter 5 . In addition, ROBYNS et al uses support vector machines, multilayer perceptrons and convolutional neural networks to identify each RF symbol of the signal frame, thus identifying 22 LoRa devices of three types 6 .…”

Section: Introductionmentioning

confidence: 99%

Hardware feature extraction technology for resource aggregation control communication channel and signal source

Li,

Shao,

Wang

et al. 2023

Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023)

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In the distributed resource access scenario, the growing number of terminal devices brings pressure and test to the wireless communication network. Unauthenticated terminal access to the network will bring huge security risks. This paper focuses on the hardware features of resource aggregation control communication channel and signal source.For the distributed power system with complex environment, this paper proposes a hardware feature extraction technology for resource aggregation oriented control communication channel and signal source.On the basis of the research on the theory of generic feature extraction of hardware fingerprint, the specific feature extraction methods of several common communication methods in typical distributed resource aggregation and control scenarios are studied. This paper designs a hardware fingerprint implementation scheme in a typical distributed resource aggregation and control scenario. Experiments show that the proposed fingerprint extraction scheme can effectively distinguish different devices according to different characteristics of devices.It provides a terminal access method with high security and low complexity in the distributed edge aggregation control network of power system.

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Measuring wireless fingerprints inside a wireless sensor network

Cited by 2 publications

References 35 publications

Wireless Fingerprints Inside a Wireless Sensor Network

Wireless Fingerprints Inside a Wireless Sensor Network

Hardware feature extraction technology for resource aggregation control communication channel and signal source

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