Performance Analysis of Deep Learning Based Non-profiled Side Channel Attacks Using Significant Hamming Weight Labeling

The use of deep learning (DL) techniques for side-channel analysis (SCA) has become increasingly popular recently. This paper assesses the application of DL to non-profiled SCA attacks on AES-128 encryption, taking into consideration various challenges, including high-dimensional data, imbalanced classes, and countermeasures. The paper proposes using a multi-layer perceptron (MLP) and a convolutional neural network (CNN) to tackle hiding protection methods, such as noise generation and de-synchronization. The paper also introduces a technique called significant Hamming weight (SHW) labeling and a dataset reconstruction approach to handle imbalanced datasets, resulting in a reduction of 30% in the number of measurements required for training. The experimental results on reconstructed dataset demonstrate improved performance in DL-based SCA compared to binary labeling techniques, especially in the face of hiding countermeasures. This leads to better results for non-profiled attacks on different targets, such as ASCAD and RISC-V microcontrollers.