Deep Learning Side-Channel Attack Against Hardware Implementations of AES

Abstract

In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside with the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled sets containing image and correct value pairs to be identified are input to a neural network, and repeatedly learning the set enables the neural network to identify objects with high accuracy. A new side-channel attack method, deep learning side-channel attack (DLSCA), utilizes the high identifying ability of the neural network to try and unveil a secret key of the cryptographic module by being trained with power waveforms and learning the leak model. However, at this stage, attacks on software implementations have been mainly investigated. In contrast, there are few studies about hardware implementations especially such as ASIC circuits. In this paper, we investigate the use of DL-SCA against hardware implementations of AES and demonstrate that it is able to unveil the secret key by applying a new technique named "mixed model dataset based on round-round XORed value." We also compare the attack performance and characteristics of DL-SCA with conventional analysis methods such as correlation power analysis and conventional template attack.