Improving the Accuracy of Differential-Neural Distinguisher for DES, Chaskey, and PRESENT

Abstract

In CRYPTO 2019, Gohr first introduced the deep learning method to cryptanalysis for Speck32/64. A differential-neural distinguisher was obtained using ResNet neural network. Zhang et al. used multiple parallel convolutional layers with different kernel sizes to capture information from multiple dimensions, thus improving the accuracy or obtaining a more round of distinguisher for Speck32/64 and Simon32/64. Inspired by Zhang's work, we apply the network structure to other ciphers. We not only improve the accuracy of the distinguisher, but also increase the number of rounds of the distinguisher, that is, distinguish more rounds of ciphertext and random number for DES, Chaskey and PRESENT.