Improved Neural Distinguishers with Multi-Round and Multi-Splicing Construction

. In CRYPTO 2019, Gohr successfully applied deep learning to diﬀerential cryptanalysis against the NSA block cipher Speck32/64, achieving higher accuracy than traditional diﬀerential distinguishers. Until now, the improvement of neural dif-ferential distinguishers is a mainstream research direction in neural-aided cryptanalysis. But the current development of training data formats for neural distinguishers forms barriers: (1) The source of data features is limited to linear combinations of ciphertexts, which does not provide more learnable features to the training samples for improving the neural distinguishers. (2) Lacking breakthroughs in constructing data format for network training from the deep learning perspective. In this paper, considering both the domain knowledge about deep learning and information on diﬀerential cryptanalysis, we use the output features of the penultimate round to proposing a two-dimensional and non-realistic input data generation method of neural diﬀerential distinguishers. Then, we validate that the proposed new input data format has excellent features through experiments and theoretical analysis. More-over, combining the idea of multiple ciphertext pairs, we generate two speciﬁc models for data input construction: MRMSP(Multiple Rounds Multiple Splicing Pairs) and MRMSD(Multiple Rounds Multiple Splicing Diﬀerences) and then build new neural distinguishers against Speck and Simon family, which eﬀectively improve the performance compared with the previous works. To the best of our knowledge, our neural distinguishers achieve the longest rounds and the higher accuracy for NSA block ciphers Speck and Simon.