信息工程大学学报2026，Vol.27Issue(4)：430-435,449,7.DOI:10.3969/j.issn.1671-0673.2026.04.007
一种融合多尺度卷积网络和自注意力结构的神经区分器
A Neural Distinguisher Integrating Multi-Scale Convolutional Network and Self-Attention Structure
摘要
Abstract
To address the low classification accuracy of traditional convolutional neural distinguishers on ciphertext data,neural distinguisher based on convolutional network and self-attention structure is proposed.Firstly,a multi-scale convolutional network is designed to learn local features of ciphertext sequences.Secondly,the self-attention mechanism is introduced after the multi-scale convolutional network to enhance the ability of the neural distinguisher to capture long-range dependencies in cipher-text sequences.Finally,the neural distinguisher is trained on multiple ciphertext datasets.Experimen-tal results show that the proposed neural distinguisher achieves higher classification accuracy than the previously superior neural distinguisher SE-ResNet on all eight ciphertext datasets.In particular,on 13-round SIMON64,the classification accuracy of the neural distinguisher is imperoved by 3.92%compared with that of SE-ResNet,demonstrating its more powerful ciphertext feature extraction capabil-ity.关键词
多尺度卷积网络/自注意力机制/神经区分器/密文序列/差分密码分析Key words
multi-scale convolutional network/self-attention mechanism/neural distinguisher/ci-phertext sequence/differential cryptanalysis分类
信息技术与安全科学引用本文复制引用
任文欢,刘天源,王林元,陈健,闫镔..一种融合多尺度卷积网络和自注意力结构的神经区分器[J].信息工程大学学报,2026,27(4):430-435,449,7.基金项目
国家自然科学基金(62271504) （62271504）
