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Hybrid Threshold Denoising Framework Using Singular Value Decomposition for Side-Channel Analysis Preprocessing.

Yuanzhen Wang1, Hongxin Zhang2,3, Xing Fang2

  • 1School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Entropy (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

A novel hybrid threshold denoising framework enhances side-channel analysis by improving signal quality. This method boosts key guessing accuracy and attack efficiency in cryptographic security.

Keywords:
Schatten normlow-rank matrix approximationnoise filteringside-channel analysissignal preprocessingsingular value decomposition

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Area of Science:

  • Cryptography
  • Signal Processing
  • Computer Security

Background:

  • Side-channel analysis relies on signal quality for successful cryptographic key recovery.
  • Acquired signals are often degraded by internal and external noise, reducing feature extraction effectiveness.
  • Preprocessing is crucial for enhancing low signal-to-noise ratio (SNR) traces in side-channel analysis.

Purpose of the Study:

  • To propose a hybrid threshold denoising framework for side-channel analysis preprocessing.
  • To improve the extraction of feature information from noisy side-channel traces.
  • To provide a general preprocessing method for non-profiled side-channel analysis.

Main Methods:

  • A hybrid threshold denoising framework based on Singular Value Decomposition (SVD).
  • Incorporation of low-rank matrix approximation theory to refine SVD rank selection.
  • Combination of hard thresholding (truncated SVD) and soft thresholding (singular value shrinkage damping).

Main Results:

  • Significant improvement in the signal-to-noise ratio (SNR) of side-channel traces.
  • Enhanced correlation between preprocessed traces and the correct cryptographic key.
  • Validation of the framework's effectiveness using public DPA contest V2 and AES_HD datasets.

Conclusions:

  • The proposed hybrid threshold denoising framework effectively preprocesses side-channel traces.
  • The method enhances trace SNR and strengthens the correlation with the correct key.
  • This approach improves the efficiency and success rate of non-profiled side-channel attacks.