Autoscaled-Wavelet Convolutional Layer for Deep Learning-Based Side-Channel Analysis

Abstract

Continuous Wavelet Transform (CWT) is rarely used in the field of side-channel analysis due to problems related to parameter (wavelet scale) selection; There is no way to find the optimal wavelet scale other than an exhaustive search, and the resulting spectrogram analysis can introduce significant analysis complexity. However, a well-scaled CWT can improve the signal-to-noise ratio of side-channel signals, which can lead to better attack performance. And our insights suggest that there is scope for CWT and deep learning approaches to be combined, which could help the models to train more effectively while overcoming the problems of CWT. In this context, we propose a novel feature extraction layer that combines a CWT with a Convolutional Neural Network (CNN). The proposed method can leverage neural network training to automatically adjust a wavelet scale, which is a critical parameter of CWT. Furthermore, the proposed method can lead to performance improvements by enabling a deep learning model to perform on- the-fly multi-frequency analysis without any pre-processing. By bringing the two approaches together, we were able to overcome the limitations of CWT and improve the performance of deep learning-based side-channel analysis. As an experimental result using open dataset ASCAD, a de facto standard in deep learning-based side-channel analysis, we confirmed that the proposed method could improve the performance by inserting the proposed layer into existing state-of-the-art deep learning models.