Lightweight block ciphers applied to the Internet of Things have been challenged by novel cryptanalysis methods. Neural cryptanalysis has become a cutting-edge method in cryptanalysis since Gohr successfully combined neural networks with differential cryptanalysis. However, how to improve the distinguishing accuracy of neural distinguishers is a major challenge in the field. Therefore, a neural distinguisher model MCPLD (Multiple Ciphertext Pairs and Leaked Differences) is proposed in this paper. The input data format of the MCPLD combines the processed leaked differences with multiple ciphert...
