A deep learning aided differential distinguisher improvement framework with more lightweight and universality

AbstractIn CRYPTO 2019, Gohr opens up a new direction for cryptanalysis. He successfully applied deep learning to differential cryptanalysis against the NSA block cipher SPECK32/64, achieving higher accuracy than traditional differential distinguishers. Until now, one of the mainstream research directions is increasing the training sample size and utilizing different neural networks to improve the accuracy of neural distinguishers. This conversion mindset may lead to a huge number of parameters, heavy computing load, and a large number of memory in the distinguishers training process. However, in the practical application of cryptanalysis, the applicability of the attacks method in a resource-constrained environment is very important. Therefore, we focus on the cost optimization and aim to reduce network parameters for differential neural cryptanalysis.In this paper, we propose two cost-optimized neural distinguisher improvement methods from the aspect of data format and network structure, respectively. Firstly, we obtain a partial output difference neural distinguisher using only 4-bits training data format which is constructed with a new advantage bits search algorithm based on t