A novel framework for extracting moment-based fingerprint features in specific emitter identification

Zhao, Yurui; Wang, Xiang; Sun, Liting; Huang, Zhitao

doi:10.1186/s13634-023-00978-4

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…In such methods, the statistics of each component are considered as the parts of the fingerprint feature vector. In a follow-up study, Jia et al also proposed to slice the communication signals by encoding symbols [14]. Those experiments suggest that the kurtosis-based features can imply emitter identification.…”

Section: Introductionmentioning

confidence: 99%

Specific emitter identification based on frequency and amplitude of the signal kurtosis

Yurui,

Xiang,

Liting

et al. 2023

J. of Syst. Eng. Electron.

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Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and further reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and amplitude of the signal kurtosis (FA-SK) is further proposed. Simulation and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Specific emitter identification based on frequency and amplitude of the signal kurtosis

Yurui,

Xiang,

Liting

et al. 2023

J. of Syst. Eng. Electron.

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“…are the Jacobian of DFT and IDFT respectively. According to (13), the DFT operation can be written in the form of matrix multiplication:…”

mentioning

confidence: 99%

Frequency-Selective Adversarial Attack Against Deep Learning-Based Wireless Signal Classifiers

Ke,

Wang,

Huang

2024

IEEE Trans.Inform.Forensic Secur.

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Although Deep learning (DL) provides state-of-art results for most spectrum sensing tasks, it is vulnerable to adversarial examples. Based on this phenomenon, we consider a noncooperative communication scenario where an intruder tries to recognize the modulation type of the intercepted signal. Specifically, this paper aims to minimize the intruder's accuracy while guaranteeing that the intended receiver can still recover the underlying message with the highest reliability. This process is implemented by adding adversarial perturbations to the channel input symbols at the encoder. In image classification, the perturbation is limited to be imperceptible to a human observer by minimizing the p norm, while in this work, we enriched the connotation of adversarial examples, and first proposed that the imperceptibility of adversarial examples in the field of wireless signals is the imperceptibility of filters. Based on this perspective, we optimized the model of adversarial examples and constrained the adversarial perturbation to a narrow frequency band so that filters cannot filter it out. We also define a new set of metrics to describe the imperceptibility of the wireless signal adversarial example. The simulation results demonstrate the viability of our approach in securing wireless communication against state-ofthe-art DL-based intruders while minimizing communication performance reduction.

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A novel framework for extracting moment-based fingerprint features in specific emitter identification

Cited by 2 publications

References 19 publications

Specific emitter identification based on frequency and amplitude of the signal kurtosis

Specific emitter identification based on frequency and amplitude of the signal kurtosis

Frequency-Selective Adversarial Attack Against Deep Learning-Based Wireless Signal Classifiers

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