CPA performance comparison based on Wavelet Transform

Abstract

Correlation Power Analysis (CPA) is a very effective attack method for finding secret keys using the statistical features of power consumption signals from cryptosystems. However, the power consumption signal of the encryption device is greatly affected or distorted by noise arising from peripheral devices. When a side channel attack is carried out, this distorted signal, which is affected by noise and time inconsistency, is the major factor that reduces the attack performance. A signal processing method based on the Wavelet Transform (WT) has been proposed to enhance the attack performance. Selecting the decomposition level and the wavelet basis is very important because the CPA performance based on the WT depends on these two factors. In this paper, the CPA performance, in terms of noise reduction and the transform domain, is compared and analyzed from the viewpoint of attack time and the minimum number of signals required to find the secret key. In addition, methods for selecting the decomposition level and the wavelet basis using the features of power consumption are proposed, and validated through experiments.

Improved Filtering Techniques for Single- and Multi-Trace Side-Channel Analysis

“…In this context, it is interesting to consider wavelet (and other) signal decomposition methods. The discrete wavelet transform (DWT) [15,18] is the sum over the duration of both scaled and shifted versions of the wavelet function. In particular, it has several metaparameters for decomposition such as the number of scaled versions and shifts, and the basis functions.…”

“…For the two, the SCA literature tends to suggest heuristic optimization techniques to pre-process the traces and reduce the noise or filter the raw traces. There are many meta-parameters including the bandwidth and frequency ranges of a filter, its shape, and the domain it is manipulating [12][13][14][15][16][17] (e.g., time, frequency or other domains, such as the wavelet domain [15,18]). Filters are utilized extensively in the field and in particular in the side-channel related literature.…”

Wavelet Support Vector Machine Algorithm in Power Analysis Attacks

“…Wavelet analysis is a powerful tool for signal processing, which is often used to approximate the target function [29]. Although wavelet analysis has been used to process noisy power traces [30][31][32], the combined effects of wavelet analysis and power analysis attacks through kernel functions have been not yet explored. In order to enhance the sparsity of wavelet approximation and the generalization of SVM, Zhang et al [33] first proposed a variant SVM algorithm based on wavelet kernel, known as wavelet SVM.…”