A deep learning-based side channel attack model for different block ciphers

Abstract

Recently, there has been renewed interest in the combination of deep learning and side-channel analysis (SCA). Many previous studies have transformed the traditional SCA into a classification problem in deep learning. This paper considers it as a regression problem based on the principle that the changes of some circuit states are related to the special operation in cipher. We proposed a regression model which consists of an initial layer, a deep feature mining dense layer, and a regression layer. In the term of dataset, there are two sources of data: the raw ASCAD power traces and the data sampled from FPGA implementation of AES and PRESENT. The mainly advantages of this model and regression task processing method is that it can adapt to different cryptographic algorithms on the same hardware device. Moreover, the experimental result that the model can significantly improve the attack accuracy of SCA. In ASCAD, its prediction accuracy achieves 2.90% and 3.63% for two different intermediate values, and their correlation coefficient evaluation 0.873, 0.840. In FPGA power dataset, their prediction and correlation coefficient are 3%, 4%, and 0.963, 0.987 respectively.