Efficient Correlation Power Analysis (CPA) Focusing on Byte-Wise Calculation Points

Abstract

Correlation Power Analysis (CPA) uses a lot of computational resources for deploying side-channel analysis to power traces. For every guessing key, it searches all data points and all power traces to reveal the secret key implemented in the encryption module. In this work, we propose a novel technique to narrow down the target region for applying CPA to the power traces. As byte-wise operations occur in every clock period, the assumed amplitude value based on the power consumption model and the actual amplitude are most correlated at the points where the peaks are observed. We extract the calculation points of each power trace using signal processing and machine learning techniques, and select the region where the CPA will be applied to. Our proposed approach achieves an approximately 20% improvement in accuracy and requires less computational resources compared to conventional CPA.