A Second Look at the ASCAD Databases

The ASCAD databases marked the starting point for a large amount of research regarding deep learning-based (SCA). While most work focuses on the analysis of different architectures, little attention has been paid to the datasets used for training and evaluation. In this paper, we provide a detailed analysis of the ASCAD datasets that examines all 16 bytes of the targeted AES implementation and reveals leakage from intermediate values of interest for attribution of Machine Learning (ML)-based SCA. We show that some bytes exhibit first-order or univariate second-order leakage that is unexpected for a protected implementation. Subsequently, we investigate how training on the fixed key we provide a detailed analysis of the ASCAD database is an easier task for (CNNs) based on two different hyperparameter architectures. Our findings suggest that results based on the we provide a detailed analysis of the ASCAD fix dataset should be revisited and that the more recent ASCAD variable dataset with variable key training should be used in future work. Finally, we investigate the attack success for all bytes. Performance differences with the same network architecture for different bytes highlight that even traces of identical operations on the same dataset pose challenges to CNNs. This highlights the possibility to use different bytes of the ASCAD dataset in order to evaluate the robustness of ML approaches in future work.