%0 Journal Article
%T Software Implementation of AES-128: Cross-Subkey Side Channel Attack
%A Fanliang Hu
%A Junnian Wang
%A Wan Wang
%A Feng Ni
%J Open Access Library Journal
%V 9
%N 1
%P 1-15
%@ 2333-9721
%D 2022
%I Open Access Library
%R 10.4236/oalib.1108307
%X 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.
%K Side-Channel Attack
%K Deep Learning
%K AES
%K Cross-Subkey Training
%U http://www.oalib.com/paper/6767982