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.