Profiled side-channel attacks are now considered as powerful forms of attacks used to break the security of cryptographic devices. A recent line of research has investigated a new profiled attack based on deep learning and many of them have used convolution neural network (CNN) as deep learning architecture for the attack. The effectiveness of the attack is greatly influenced by the CNN architecture. However, the CNN architecture used for current profiled attacks have often been based on image recognition fields, and choosing the right CNN architectures and parameters for adaption to profiled attacks is still challenging. In this paper, we propose an efficient profiled attack for on unprotected and masking-protected cryptographic devices based on two CNN architectures, called CNNn, CNNd respectively. Both of CNN architecture parameters proposed in this paper are based on the property of points of interest on the power trace and further determined by the Grey Wolf Optimization (GWO) algorithm. To verify the proposed attacks, experiments were performed on a trace set collected from an Atmega8515 smart card when it performs AES-128 encryption, a DPA contest v4 dataset and the ASCAD public dataset
MLP and CNN-based Classification of Points of Interest in Side-channel Attacks
