Side channel attack preprocessing method based on wavelet airspace correlation method

Abstract

The present invention discloses a side channel attack preprocessing method based on a wavelet airspace correlation method. Collected power consumption signals f(n) are subjected to denoising through awavelet airspace correlation method and then is subjected to side channel analysis. According to the technical scheme, the interference of noise for a side channel attack result is effectively reduced, and the side channel attack success rate and the efficiency are improved.

Description

Technical field [0001] The invention belongs to the technical field of cryptography, and relates to a side channel attack preprocessing method. The collected power consumption signal is subjected to wavelet spatial correlation method denoising processing before implementing related side channel attacks. Background technique [0002] The commonly used method in side-channel attack technology is power analysis, and the collected power consumption signal is easily affected by noise, and the key acquisition of power consumption analysis is based on the collected power consumption signal, which is one of the most important factors affecting the success rate of analyzing the key. It is the signal-to-noise ratio of the real power consumption signal, so whether the noise can be effectively removed is the key to improving the success rate of power analysis. To solve this problem, a side-channel attack preprocessing technology based on wavelet spatial correlation method is proposed to perf...

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Problems solved by technology

However, these methods generally have many deficiencies. For example, the low-pass filter method is to transform the noisy signal through Fourier transform, and filter out the frequency components contained in the noise by selecting an appropriate filter. The limitation of this method is that it requires a useful The less overlap between the frequency of the signal and the frequency of the noise, the better, and it is obviously insufficient for white noise, pulse signals, non-stationary process signals, etc.; the fourth-order cumulant method has high computational complexity and requires that the noise must conform to Gaussian distribution, the two-sided restriction also reduces the effectiveness of the fourth-order cumulant method

The biggest disadvantage of principal component analysis is the high computational complexity and time-consuming when solving eigenvectors and eigenvalues, and the empirical mode decomposition method also has the problem of high computational complexity; principal component analysis has a fatal shortcoming: it is in In the process of solving eigenvectors and eigenvalues, it is necessary to obtain an autocorrelation matrix to complete the decomposition, and the computational complexity and storage space involved in this process are too large for normal computing equipment, which greatly limits the performance of principal component analysis. Effectiveness in Consumption Analysis

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