Behavior Modeling and Individual Recognition of Sonar Transmitter for Secure Communication in UASNs

Shi, Fan; Chen, Zhenlei; Cheng, Xiaochun

doi:10.1109/access.2019.2923059

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…Cognitive capabilities provided by the model include reasoning, problem solving, planning, and learning from experience. The cognitive capabilities relate to monitoring performance on related fog nodes and IoT devices to determine how best to allocate resources, transmit data, and detect malicious activity based upon analysis of current and previous performance Shi et al [2019] Balasubramanian et al [2019b] Ali and Cheng [2019]. These are important considerations in the provision of medical and healthcare service via smart networks.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

Al-Khafajiy

Otoum

Baker

et al. 2021

ACM Trans. Internet Technol.

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There have been significant advances in the field of Internet of Things (IoT) recently. In most IoT based systems or applications, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT...

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Section: Introductionmentioning

confidence: 99%

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

Al-Khafajiy

Otoum

Baker

et al. 2021

ACM Trans. Internet Technol.

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“…In this study, ten approximate sonar transmitters were modeled using memory polynomials, and the power spectrum estimation of output signals was used as a fingerprint to identify the transmitters. When the input signal was a two-tone signal and the SNR was 0 dB, the recognition rate reached 100% [18]. Nonlinear modeling of a single device may not be sufficient to characterize the nonlinear behavior of a transmitter.…”

Section: Introductionmentioning

confidence: 99%

Individual Identification of Radar Emitters Based on a One-Dimensional LeNet Neural Network

Chen¹,

Wu²,

Lei³

2021

Symmetry

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Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter through processing the received signals. By identifying the extracted fingerprint features, one can also identify the emitter to which the received signals belong. Due to differences in transmitter hardware, this fingerprint cannot be duplicated. Therefore, SEI plays an important role in the field of information security and can reduce the information leakages caused by key theft. This method can also be used in the military field to support communication countermeasures via emitter individual identification. In this paper, empirical mode decomposition is carried out for each radar pulse signal, and then the bispectral features are extracted. Dimensionality reduction is carried out according to the symmetry of the bispectral features. The features after dimensionality reduction are input into a one-dimensional LeNet neural network as the fingerprint features of the emitter, and the identification of 10 radar emitter sources is completed. Based on the verification of real signals, the SEI identification strategy in this paper achieved a recognition rate of 96.4% for 10 radar signals, 98.9% for 10 data emitter signals, and 88.93% for 5 communication radio signals.

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“…Aiming at the nonlinear error introduced by DAC [ 18 ], Polak used a random Brownian bridge process to model the nonlinear behavior of the radiator transmitter’s DAC device [ 19 ]. Zhang and Liu used a memoryless polynomial model to describe the power-amplifier nonlinearity of different individual radiation sources [ 20 , 21 , 22 ], which simplified the solution of the nonlinear power-amplifier model, but insufficiently describing the memory effect of the power amplifier. To characterize the nonlinear behavior of a power amplifier with a weak memory effect [ 23 ], Liu presented a radio-frequency front-end nonlinearity estimator that performed SEI based on the knowledge of a training sequence.…”

Section: Introductionmentioning

confidence: 99%

Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier

Chen

Lei

2021

Sensors

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Specific transmitter identification (SEI) is a technology that uses a received signal to identify to which individual radiation source the transmitted signal belongs. It can complete the identification of the signal transmitter in a non-cooperative scenario. Therefore, there are broad application prospects in the field of wireless-communication-network security, spectral resource management, and military battlefield-target communication countermeasures. This article demodulates and reconstructs a digital modulation signal to obtain a signal without modulator distortion and power-amplifier nonlinearity. Comparing the reconstructed signal with the actual received signal, the coefficient representation of the nonlinearity of the power amplifier and the distortion of the modulator can be obtained, and these coefficients can be used as the fingerprint characteristics of different transmitters through a convolutional neural network (CNN) to complete the identification of specific transmitters. The existing SEI strategy for changing the modulation parameters of a test signal is to mix part of the test signal with the training signal so that the classifier can learn the signal of which the modulation parameter was changed. This method is still data-oriented and cannot process signals for which the classifier has not been trained. It has certain limitations in practical applications. We compared the fingerprint features extracted by the method in this study with the fingerprint features extracted by the bispectral method. When SNR < 20 dB, the recognition accuracy of the bispectral method dropped rapidly. The method in this paper still achieved 86% recognition accuracy when SNR = 0 dB. When the carrier frequency of the test signal was changed, the bispectral feature failed, and the proposed method could still achieve a recognition accuracy of about 70%. When changing the test-signal baud rate, the proposed method could still achieve a classification accuracy rate of more than 70% for four different individual radiation sources when SNR = 0 dB.

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Behavior Modeling and Individual Recognition of Sonar Transmitter for Secure Communication in UASNs

Cited by 13 publications

References 30 publications

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

Individual Identification of Radar Emitters Based on a One-Dimensional LeNet Neural Network

Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier

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