A systematic study of data augmentation for protected AES implementations

Side-channel attacks against cryptographic implementations are mitigated by the application of masking and hiding countermeasures. Hiding countermeasures attempt to reduce the Signal-to-Noise Ratio of measurements by adding noise or desynchronization effects during the execution of the cryptographic operations. To bypass these protections, attackers adopt signal processing techniques such as pattern alignment, filtering, averaging, or resampling. Convolutional neural networks have shown the ability to reduce the effect of countermeasures without the need for trace preprocessing, especially alignment, due to their shift invariant property. Data augmentation techniques are also considered to improve the regularization capacity of the network, which improves generalization and, consequently, reduces the attack complexity. In this work, we deploy systematic experiments to investigate the benefits of data augmentation techniques against masked AES implementations when they are also protected with hiding countermeasures. Our results show that, for each countermeasure and dataset, a specific neural network architecture requires a particular data augmentation configuration to achieve significantly improved attack performance. Our results clearly show that data augmentation should be a standard process when targeting datasets with hiding countermeasures in deep learning-based side-channel attacks.