Understanding Neural Networks in Profiled Side-Channel Analysis

Side-channel analysis (SCA) capitalizes on unintentionally leaked information to extract sensitive data from cryptographic systems. Over recent years, deep learning has shown effectiveness in analyzing the diverse forms of SCA signals. However, due to the absence of a comprehensive understanding, constructing effective networks tailored for a variety of cryptographic systems becomes a considerable challenge. This paper proposes a novel methodology designed to deconstruct networks intended for SCA, with the goal of enhancing our understanding of the mechanisms by which these complex systems process diverse SCA signals. Our approach begins with a f-ANOVA-based method to pinpoint pivotal parameter amidst a plethora of adjustable ones. Thereafter, network visualization technique is harnessed to investigate the impact of variations in these key parameters. Through experiments, we have distilled principles for network formulation that accommodate the unique characteristics inherent in side-channel signals. The experimental outcomes highlight notable improvements when parameters are set according to the proposed principles.