Specific Emitter Identification Techniques for the Internet of Things

Sa, Kejin; Lang, Dapeng; Wang, Chenggang; Bai, Yu

doi:10.1109/access.2019.2962626

Cited by 31 publications

(14 citation statements)

References 33 publications

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“…Specific Emitter Identification (SEI) is one technique capable of filling this gap in security [15], [16]. SEI is a physical layer approach that has demonstrated success in identifying wireless transmitters by exploiting the unintentional coloration that is imparted upon every waveform during its formation and transmission [17]- [35].…”

Section: Introductionmentioning

confidence: 99%

Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning

2021

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The Internet of Things (IoT) is here and has permeated every aspect of our lives. A disturbing fact is that the majority of all IoT devices employ weak or no encryption at all. This coupled with recent advances within the areas of computational power and deep learning has increased interest in Specific Emitter Identification (SEI) as an effective means of IoT security. Deep learning is capable of in-situ extraction of discriminating features, making it well suited to discrimination of wireless transmitters without the need for feature engineering. However, the accuracy of the deep learning model is adversely affected by time-varying channel conditions. The time-varying nature is attributed to the mobility of the transmitter, receiver, objects within the operations environment, or combinations thereof. This can result in the channel conditions changing faster than the deep learning algorithm is capable of handling. This paper assesses deep learning-based SEI using waveforms that undergo Rayleigh fading, as well as channel estimation and equalization, prior to being input into a deep learning algorithm.

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Section: Introductionmentioning

confidence: 99%

Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning

2021

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“…The authors in [6] and [7] extract the energy entropy and color moments as identification features to cope with the SEI problems in both single-hop and relaying scenarios. The authors in [8] utilize power spectral density and adjacent channel power ratio (ACPR) as fingerprint features. Then, the principal component analysis (PCA) is employed to reduce the dimension of features.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Curve Fitting and BP Neural Network With Feature Extraction for Mobile Specific Emitter Identification

2021

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As a critical technology in both civil and military fields, specific emitter identification (SEI) can identify signal sources according to their various features. Existing methods on SEI are mostly based on the prior knowledge of emitters, which are powerless in the non-cooperative scenario. In order to realize the unsupervised identification, the mobile SEI method based on fingerprint set construction and feedback classification algorithm is proposed in this paper. The proposed method first divides signal fingerprints into static features and dynamic features, where the former describe the inherent features of emitters, and the latter represent the moving state features of emitters. Then, the feedback classification algorithm composed of dynamic curve fitting and back propagation (BP) neural network is applied in the classification of signals.The dynamic curve accomplishes the first classification and the results with high credibility are used to train the BP neural network which accomplishes the final classification. Simulation results demonstrate that the proposed method can complete the identification of mobile specific emitter sources in the unsupervised state with more than 95% identification rate.INDEX TERMS SEI, curve fitting, signal fingerprint, BP neural network.

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“…Pan [12] inputted the Hilbert spectrum into the deep residual network and found that it has better identification under various channel conditions. Sa [22] converted I/Q signals into Contour Stella images and inputted them into a Convolutional Neural Network (CNN) for classification. Wu [23] proposed a Recurrent Neural Network (RNN) recognition algorithm based on long and short-term memory, and found that it achieved high recognition accuracy under the condition of low signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification Based on Multi-Domain Feature Fusion and Integrated Learning

Liu

Huang

et al. 2021

Symmetry

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Specific Emitter Identification (SEI) is a key research problem in the field of information countermeasures. It is one of the key technologies required to be solved urgently in the target reconnaissance system. It has the ability to distinguish between different individual radiation sources according to the varying individual characteristics of the emitter hardware within the transmitted signals. In response to the lack of scarcity among labeled samples in specific emitter identification, this paper proposes a method combining multi-domain feature fusion and integrated learning (MDFFIL). First, the received signal is preprocessed to obtain segmented time domain signal samples. Then, the signal is converted to time–frequency distribution using wavelet transform. Afterwards, an integrated learning two-stage recognition classification method is designed to extract data features of 1D time domain signals and 2D time–frequency distribution signals using the symmetry network structures of CVResNet and ResNet. Finally, fused features are fed into the complex-valued residual network classifier to obtain the final classification results. We demonstrate through the analysis results of the measured data that the proposed method has a higher accuracy as compared with the classical feature extraction method, and that this can improve the identification of communication radiation sources with fewer labeled samples.

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Specific Emitter Identification Techniques for the Internet of Things

Cited by 31 publications

References 33 publications

Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning

Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning

Dynamic Curve Fitting and BP Neural Network With Feature Extraction for Mobile Specific Emitter Identification

Specific Emitter Identification Based on Multi-Domain Feature Fusion and Integrated Learning

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