A Highly Efficient Neural Distinguisher Framework for IoT-Friendly Lightweight SPN Block Ciphers
JiaShuo Liu, Manman Li, Jiongjiong Ren, Shaozhen Chen · IEICE Transactions on Information and Systems · 2025
In the past few years, research on lightweight block ciphers as security ciphers in the Internet of Things (IoT) has attracted considerable attention in cryptography. In this paper, we present an improved framework for neural distinguishers in lightweight SPN block ciphers suitable for IoT, focusing on two aspects: training data format and neural network structure. First, we analyze the nature of the SPN round function, then divide it into three cases to apply data augmentation. Second, we generate training data samples using three dimensions and construct neural networks using two-dimensional convolution. Finally, we validate the advantages of the improved framework on the SKINNY family and MIDORI family with higher accuracy and achieve a breakthrough in the number of rounds.