International Association
for Cryptologic Research

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 cryptanalysis more easily. In this paper, we report a generic tool using deep neural classifier that assists to find differential distinguishers for block ciphers with reduced round. We apply this approach for the differential cryptanalysis of ARX- based encryption schemes HIGHT, LEA, and SPARX. The result shows that our deep learning based distinguishers work with high accuracy for 14-round HIGHT, 13-Round LEA and 11-round SPARX. We also achieve an improvement of the lower bound of data complexity for these three ARX based ciphers.