SoK: Deep Learning-based Physical Side-channel Analysis

Deep learning-based side-channel analysis (DLSCA) represents a powerful paradigm for running side-channel attacks. DLSCA, in a state-of-the-art setting, can break multiple targets with a single attack trace, requiring minimal feature engineering. As such, DLSCA is an extremely active research area for both industry and academia. At the same time, due to domain activity, it becomes more difficult to understand the current trends and challenges. In this systematization of knowledge, we provide a critical overview of developments in DLSCA over the past years, enabling us to offer concrete suggestions and taxonomies. Moreover, we examine the reproducibility perspective and show that achieving it in full is difficult and requires a careful experimental setup.