Overview of Side Channel Analysis Based on Convolutional Neural Network

Abstract

The profiled side-channel analysis method can effectively attack the implementation of cryptographic,and the side-channel cryptanalysis method based on convolutional neural network (CNNSCA) can efficiently carry out cryptographic attacks,and even can attack the implementation of protected encryption algorithms.In view of the current research status of side-channel cryptanalysis profiling methods,this paper compares and analyzes the characteristics and performance differences of several CNNSCA models,and focuses on the typical CNN model structure and side-channel signal public data set ASCAD.Through model comparison and experimental results,it compares and analyzes the effects of different CNN network modeling methods,and then analyzes the performance factors that affect the CNNSCA method and the advantages of the side-channel profiling method based on convolutional neural networks.Research and analysis show that CNNSCA based on VGG variants performs best in generalization and robustness when attacking target data sets in various situations,but whether the training level of the used CNN model and the hyperparameter settings are most suitable for SCA scenarios have not been verified.In the future,researchers can improve the classification accuracy and decryption performance of CNNSCA by adjusting various hyperparameters of the CNN model,use data enhancement techniques and combine the excellent CNN network in the Imagenet competition to explore the most suitable CNN model for SCA scenarios,which is a development trend.

Keywords

• side-channel analysis|profiling method|convolutional neural network|hyperparameter|performance evaluation