  In recent years, the convolutional neural networks (CNNs) have received a lot
of interest in the side-channel community. The previous work has shown that
CNNs have the potential of breaking the cryptographic algorithm protected with
masking or desynchronization. Before, several CNN models have been exploited,
reaching the same or even better level of performance compared to the
traditional side-channel attack (SCA). In this paper, we investigate the
architecture of Residual Network and build a new CNN model called attention
network. To enhance the power of the attention network, we introduce an
attention mechanism - Convolutional Block Attention Module (CBAM) and
incorporate CBAM into the CNN architecture. CBAM points out the informative
points of the input traces and makes the attention network focus on the
relevant leakages of the measurements. It is able to improve the performance of
the CNNs. Because the irrelevant points will introduce the extra noises and
cause a worse performance of attacks. We compare our attention network with the
one designed for the masking AES implementation called ASCAD network in this
paper. We show that the attention network has a better performance than the
ASCAD network. Finally, a new visualization method, named Class Gradient
Visualization (CGV) is proposed to recognize which points of the input traces
have a positive influence on the predicted result of the neural networks. In
another aspect, it can explain why the attention network is superior to the
ASCAD network. We validate the attention network through extensive experiments
on four public datasets and demonstrate that the attention network is efficient
in different AES implementations.
