Deep Learning Techniques for Side-Channel Analysis on AES Datasets Collected from Hardware and Software Platforms.

Side-channel analysis (SCA) is launched by exploiting the information leaking from the implementation a cryptographic algorithm, e.g., power consumption information. Recently, deep learning-based SCA techniques have also facilitated SCA against software and hardware implementations of various cryptographic algorithms. In this work, we perform SCA using various deep learning (DL) techniques such as Multilayered Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) on the datasets collected from hardware and software platforms. The objective of this work is to identify the performance of DL techniques in performing SCA for secret key recovery and finding out the best settings for the model to optimize the attack performance in terms of on computation time and SCA efficiency. In our study, we have focused on two open-source AES-128 encryption algorithm databases, ASCAD and DPA contest v2 (DPAv2), where ASCAD database consists of the power traces captured from a software implementation of the AES and DPAv2 database consists of the power traces captured a hardware implementation of the AES. For the first time, we applied hyperparameter tuning with Bayesian Optimization and distributed computing on ASCAD database and we investigated the impact of MLP and RNN along with the distributed computing and hyperparameter tuning with Bayesian optimization on DPAv2 database. Our results show that the CNNs are the best models for performing the attack on software implementation while MLPs are the best for attacking hardware implementation of cryptographic algorithms.