An efficient differential analysis method based on deep learning

Abstract

Differential analysis is a vital tool for evaluating the security of cryptography algorithms. There has been a growing interest in the differential distinguisher based on deep learning. Various neural network models have been created to increase the accuracy of distinguishing between ciphertext and random sequences. However, few studies have focused on differential analysis at the design stage of cryptographic algorithms. This paper presents an appropriate model for differential analysis of block ciphers. The model is similar to multilayer perceptron (MLP) models in simplicity and clarity. It also introduces a shortcut connection that enables one to learn more information about the differential analysis dataset. The model is used to predict the minimum number of active S-boxes (AS), linking differential analysis results to algorithm features. This model and two classical neural network models are compared under fair experimental conditions. The findings indicate that our model predicts the AS values with an accuracy of 97%. It can effectively predict the results of differential analysis. In addition, the differential analysis dataset is constructed for SPN structure cryptographic algorithms. It can be used for further differential analysis studies based on deep learning.