Improving deep learning-based neural distinguisher with multiple ciphertext pairs for Speck and Simon

The neural network-based differential distinguisher has attracted significant interest from researchers due to its high efficiency in cryptanalysis since its introduction by Gohr in 2019. However, the accuracy of existing neural distinguishers remains limited for high-round-reduced cryptosystems. In this work, we explore the design principles of neural networks and propose a novel neural distinguisher based on a multi-scale convolutional block and dense residual connections. Two different ablation schemes are designed to verify the efficiency of the proposed neural distinguisher. Additionally, the concept of a linear attack is introduced to optimize the input dataset for the neural distinguisher. By combining ciphertext pairs, the differences between ciphertext pairs, the keys, and the differences between the keys, a novel dataset model is designed. The results show that the accuracy of the proposed neural distinguisher, utilizing the novel neural network and dataset, is 0.15–0.45% higher than Gohr’s distinguisher for Speck 32/64 when using a single ciphertext pair as input. When using multiple ciphertext pairs as input, it is 1.24–3.5% higher than the best distinguishers for Speck 32/64 and 0.32–1.83% higher than the best distinguishers for Simon 32/64. Finally, a key recovery attack based on the proposed neural distinguisher using a single ciphertext pair is implemented, achieving a success rate of 61.8%, which is 9.7% higher than the distinguisher proposed by Gohr. Therefore, the proposed neural distinguisher demonstrates significant advantages in both accuracy and key recovery rate.