Hou Yufei, Liu Jie, Han Shouxu, Ma Zhongjun, Ye Xi, Nie Xuan
School of Software, Northwestern Polytechnical University, Xi'an, 710000, China.
Information Security Research Center, CEPREI, Guangzhou, 511370, China.
Sci Rep. 2025 Apr 21;15(1):13696. doi: 10.1038/s41598-025-98251-1.
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.
自2019年戈尔提出基于神经网络的差分区分器以来，因其在密码分析中的高效性而引起了研究人员的极大兴趣。然而，对于高轮次简化的密码系统，现有神经区分器的准确性仍然有限。在这项工作中，我们探索了神经网络的设计原则，并提出了一种基于多尺度卷积块和密集残差连接的新型神经区分器。设计了两种不同的消融方案来验证所提出的神经区分器的效率。此外，引入了线性攻击的概念来优化神经区分器的输入数据集。通过组合密文对、密文对之间的差异、密钥以及密钥之间的差异，设计了一种新型数据集模型。结果表明，当使用单个密文对作为输入时，所提出的利用新型神经网络和数据集的神经区分器对于Speck 32/64的准确率比戈尔的区分器高0.15 - 0.45%。当使用多个密文对作为输入时，它比Speck 32/64的最佳区分器高1.24 - 3.5%，比Simon 32/64的最佳区分器高0.32 - 1.83%。最后，实现了基于所提出的神经区分器使用单个密文对的密钥恢复攻击，成功率达到61.8%，比戈尔提出的区分器高9.7%。因此，所提出的神经区分器在准确率和密钥恢复率方面都显示出显著优势。
