Deep learning based side-channel analysis (DL-SCA) has achieved remarkable success in recovering cryptographic keys from embedded devices by exploiting physical leakages such as power consumption and electromagnetic emissions, posing a serious threat to the security of cryptographic implementations. However, a major challenge arises in cross-device attacks, where a model trained on profiling devices cannot be directly applied to attack a different device. This is because domain discrepancies emerge from variations in chip architecture, manufacturing, operational conditions, and data acquisition methods between these two devices. Many existing DL-SCA schemes have not adequately addressed the challenges posed by the differences. Therefore, we propose a novel cross-device SCA framework based on an autoencoder, which leverages the encoder–decoder architecture to align feature distributions across devices in the latent space while simultaneously preserving discriminative leakage features through the reconstruction process. To achieve this, Maximum Mean Discrepancy (MMD) is integrated into the loss function and applied to the latent representations, effectively narrowing the distribution gap between profiling and attack devices. Operating in the latent space allows our approach to avoid the training instability of adversarial methods and provides an efficient end-to-end solution for domain alignment. Building on this framework, we further introduce three multi-domain adaptation methods. Experimental results, evaluated in terms of Partial Guessing Entropy (PGE), demonstrate that cross-device attacks can be effectively executed even with device discrepancies, with most keys being successfully recovered within 1000 traces. Moreover, the proposed adaptation techniques significantly reduce the number of traces required for successful key recovery.
Du et al. (Tue,) studied this question.