西安电子科技大学学报（自然科学版）2025，Vol.52Issue(1)：196-214,19.DOI:10.19665/j.issn1001-2400.20241001
神经网络差分区分器的改进方案与应用
Improved schemes and applications of the neural network differential distinguisher
摘要
Abstract
In order to further study the application of deep learning in cryptographic security analysis,neural networks are used for differential analysis of lightweight block cryptography.The following four research results are obtained.First,a neural network differential distinguisher is constructed by using a deep residual network with an attention mechanism,and applied to three types of lightweight block ciphers:SIMON,SIMECK and SPECK.The results show that the effective distinguisher of SIMON32/64 and SIMECK32/64 can reach up to 11 rounds,and the accuracy is 0.5172 and 0.5164,respectively.The SPECK32/64 has an effective distinguisher of up to 8 rounds with an accuracy of 0.5868.Second,the influence of different input differences on the accuracy of the neural network differential distinguisher is explored.For SIMON,SIMECK and SPECK ciphers,the accuracy of the neural network differential distinguisher corresponding to different input differences is obtained by using the fast training of neural networks.The results show that the input difference with a low Hamming weight and high probability can improve the accuracy of the neural network differential distinguisher.At the same time,the suitable input difference for the SIMON32/64,SIMECK32/64 and SPECK32/64 neural network differential distinguisher is found to be 0x0000/0040,0x0000/0001 and 0x0040/0000,respectively.Third,the influence of the input data format containing different information on the accuracy of the neural network differential distinguisher is explored.Changing the amount of information contained in the input data according to the characteristics of the cryptographic algorithm and retraining the corresponding neural network differential distinguisher.The results show that,compared to a neural network differential distinguisher that only includes ciphertext pair information,those that incorporate both ciphertext pair information and differential information from the penultimate round achieve a higher accuracy.Fourth,on the basis of the above research,the last wheel key recovery attack is carried out on 11 rounds of SIMON32/64.When 29 plaintext-ciphertext pairs are selected,the attack success rate in 100 attacks can reach 100％.关键词
神经网络/密码学/轻量级分组密码/差分密码分析/注意力机制/神经网络差分区分器/密钥恢复攻击Key words
neural networks/cryptography/lightweight block cipher/differential cryptanalysis/attention mechanism/neural network differential distinguisher/key recovery attack分类
信息技术与安全科学引用本文复制引用
栗琳轲,陈杰,刘君..神经网络差分区分器的改进方案与应用[J].西安电子科技大学学报（自然科学版）,2025,52(1):196-214,19.基金项目
国家自然科学基金(62302285) （62302285）
河南省网络密码技术重点实验室研究课题(LNCT2022-A08) （LNCT2022-A08）
