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Yang, G., Li, H., Ming, J. and Zhou, Y. (2019) CDAE: Towards Empowering Denoising in Side-Channel Analysis. In: Zhou, J., Luo, X., Shen, Q. and Xu, Z., Eds., International Conference on Information and Communications Security, Springer, Cham, 269-286. https://doi.org/10.1007/978-3-030-41579-2_16

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• TITLE: Software Implementation of AES-128: Cross-Subkey Side Channel Attack

  AUTHORS: Fanliang Hu, Junnian Wang, Wan Wang, Feng Ni

  KEYWORDS: Side-Channel Attack, Deep Learning, AES, Cross-Subkey Training

  JOURNAL NAME: Open Access Library Journal, Vol.9 No.1, January 27, 2022

  ABSTRACT: 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.