The majority of recently demonstrated Deep-Learning Side-Channel Attacks (DLSCAs) use neural networks trained on a segment of traces containing operations only related to the target subkey. However, when the number of training traces is restricted such as in the ASCAD database, deep-learning models always suffer from overfitting since the insufficient training data. One data-level solution is called data augmentation, which is to use the additional synthetically modified traces to act as a regularizer to provide a better generalization capacity for deep-learning models. In this paper, we propose a cross-subkey training approach which acts as a trace augmentation. We train deep-learning models not only on a segment of traces containing the SBox operation of the target subkey of AES-128, but also on segments for other 15 subkeys. We show that training a network model by combining different subkeys outperforms a traditional network model trained with a single subkey, and prove the conclusion on two well-known datasets.
Cite this paper
Hu, F. , Wang, J. , Wang, W. and Ni, F. (2022). Software Implementation of AES-128: Cross-Subkey Side Channel Attack. Open Access Library Journal, 9, e8307. doi: http://dx.doi.org/10.4236/oalib.1108307.
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