Abstract: As a critical method for block cipher security evaluation,differential cryptanalysis distinguishes ciphers from random permutations by analyzing plaintext difference propagation during encryption.Traditional approaches struggle with complex cryptographic algorithms,while deep learning offers new cryptanalysis perspectives increasingly applied in recent years.To enhance the security evaluation of block ciphers,this paper proposes a neural differential distinguisher construction method that integrates traditional differential analysis with deep learning.For dataset construction,a triplet input format comprising multiple ciphertext pairs is adopted to preserve differential characteristics and capture cross-ciphertext-pair correlations.The network architecture builds upon Convolutional Neural Networks(CNNs) and incorporates residual shrinkage networks to form a deep expansion structure with a multi-scale feature fusion mechanism.Experiments on GIFT-128 and ASCON-PERMUTATION demonstrate significant improvements:For GIFT-128,the highest accuracy of 6-round and 7-round distinguishers reaches 99.70%(an improvement of 9.30%) and 95.47%(an improvement of 13.09%),respectively.For the 4-round analysis of ASCON,the highest accuracy achieves 53.54%.These results validate the effectiveness of the deep learning approach in cryptographic security analysis.

Key words: Deep learning, Differential cryptanalysis, Block cipher, Neutral distinguisher, GIFT-128, ASCON