Paper 2022/1195 Original: eprint.iacr.org/2022/1195 ↗

A Deep Neural Differential Distinguisher for ARX based Block Cipher

Abstract

Over the last few years, deep learning is becoming the most trending topic for the classical cryptanalysis of block ciphers. Differential cryptanalysis is one of the primary and potent attacks on block ciphers. Here we apply deep learning techniques to model differential cryptanaly- sis more easily. In this paper, we report a generic tool called NDDT1, us- ing deep neural classifier that assists to find differential distinguishers for symmetric block ciphers with reduced round. We apply this approach for the differential cryptanalysis of ARX-based encryption schemes HIGHT, LEA, SPARX and SAND. To the best of our knowledge, this is the first deep learning-based distinguisher for the mentioned ciphers. The result shows that our deep learning based distinguishers work with high accuracy for 14-round HIGHT, 13-Round LEA, 11-round SPARX and 14-round SAND128. The relationship between the hamming weight of input difference of a neural distinguisher and the corresponding maxi- mum round number of the cipher has been justified through exhaustive experimentation. The lower bounds of data complexity for differential cryptanalysis have also been improved.