A Practical Non-Profiled Deep-Learning-Based Power Analysis with Hybrid-Supervised Neural Networks

Abstract

With the rapid advancement of deep learning, the neural network has become the primary approach for non-profiled side-channel attacks. Nevertheless, challenges arise in practical applications due to noise in collected power traces and the substantial amount of data required for training deep learning neural networks. Additionally, acquiring measuring equipment with exceptionally high sampling rates is difficult for average researchers, further obstructing the analysis process. To address these challenges, in this paper, we propose a novel architecture for non-profiled differential deep learning analysis, employing a hybrid-supervised neural network. The architecture incorporates a self-supervised autoencoder to enhance the features of power traces before they are utilized as training data for the supervised neural network. Experimental results demonstrate that the proposed architecture not only outperforms traditional differential deep learning networks by providing a more obvious distinction, but it also achieves key discrimination with reduced computational costs. Furthermore, the architecture is evaluated using small-scale and downsampled datasets, confirming its ability recover correct keys under such conditions. Moreover, the altered architecture designed for data resynchronization was proved to have the ability to distinguish the correct key from small-scale and desynchronized datasets.