Wavelet de-noising method in the side-channel attack

Li, Jun; Li, Shaoqing; Shi, Yubo; Zhou, Errui

doi:10.1109/icspcc.2015.7338933

Cited by 1 publication

(2 citation statements)

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“…The rapid advancement in deep learning has injected new vitality into signal detection and recognition [29][30][31]. Ke et al [32] employ convolutional long short-term deep neural networks (CLDNNs) cascaded with convolutional neural networks (CNNs) and long short-term memory (LSTM) to extract time-domain and frequency-domain features from input signal sequences.…”

Section: Deep-learning-based Signal Detectionmentioning

confidence: 99%

“…Furthermore, the quantity of labeled samples is considered a crucial factor in evaluating performance, as it is challenging to obtain under non-collaborative conditions. We investigated the performance of TATR across all SNRs with a proportion of labeled samples in the training dataset, only χ = [5,10,20,30,40,60, 80, 100(%)] samples have labels in the training set. As depicted in Figure 9b, the model's performance improves as the quantity of available labeled samples increases.…”

Section: Validity Analysis and Ablation Experimentsmentioning

confidence: 99%

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Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR

Li,

Shi,

Wang

et al. 2024

Remote Sensing

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In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&C) signals often become submerged in background noise. Traditional TT&C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms often rely on expert-experience-driven post-processing steps, failing to achieve end-to-end signal detection. To address the aforementioned limitations of existing algorithms, we propose an intelligent satellite TT&C signal detection method based on triplet attention and Transformer (TATR). TATR introduces the residual triplet attention (ResTA) backbone network, which effectively combines spectral feature channels, frequency, and amplitude dimensions almost without introducing additional parameters. In signal detection, TATR employs a multi-head self-attention mechanism to effectively address the long-range dependency issue in spectral information. Moreover, the prediction-box-matching module based on the Hungarian algorithm eliminates the need for non-maximum suppression (NMS) post-processing steps, transforming the signal detection problem into a set prediction problem and enabling parallel output of the detection results. TATR combines the global attention capability of ResTA with the local self-attention capability of Transformer. Experimental results demonstrate that utilizing only the signal spectrum amplitude information, TATR achieves accurate detection of weak TT&C signals with signal-to-noise ratios (SNRs) of −15 dB and above (mAP@0.5 > 90%), with parameter estimation errors below 3%, which outperforms typical target detection methods.

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