Model design and parameter optimization of CNN for side-channel cryptanalysis.

ABSTRACT:

Background

The side-channel cryptanalysis method based on convolutional neural network (CNNSCA) can effectively carry out cryptographic attacks. The CNNSCA network models that achieve cryptanalysis mainly include CNNSCA based on the VGG variant (VGG-CNNSCA) and CNNSCA based on the Alexnet variant (Alex-CNNSCA). The learning ability and cryptanalysis performance of these CNNSCA models are not optimal, and the trained model has low accuracy, too long training time, and takes up more computing resources. In order to improve the overall performance of CNNSCA, the paper will improve CNNSCA model design and hyperparameter optimization.

Methods

The paper first studied the CNN architecture composition in the SCA application scenario, and derives the calculation process of the CNN co

SUBMITTER: Liu YL 

PROVIDER: S-EPMC8771786 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature