摘要
为了进一步研究神经网络在密码分析方面的应用,利用深度残差网络和传统差分密码分析技术构造并改进了几类典型的轻量级分组密码算法的神经网络差分区分器。主要取得以下结果:①分别构造了4~7轮PRESENT、3轮KLEIN、7~9轮LBlock和7~10轮Simeck 32/64的神经网络差分区分器,并基于密码的分组结构分别进行了分析;②基于SPN结构分组密码的特点对PRESENT和KLEIN的神经网络差分区分器进行了改进,最多可提高约5.12%的准确率,并在对LBlock的神经网络差分区分器进行研究时验证得出这种改进方式不适用于Feistel结构的分组密码;③基于Simeck 32/64本身密码算法的特点对其神经网络差分区分器进行改进,提高了约2.3%的准确率。同时,将Simeck 32/64的改进方法与多面体差分分析进行结合,将已有的8轮和9轮Simeck 32/64多面体神经网络差分区分器的准确率提高了约1%和3.2%。最后,将实验中得到的3类神经网络差分区分器模型分别应用到11轮Simeck 32/64的最后一轮子密钥恢复攻击中,其中最佳的实验结果是在1000次攻击实验中以26.6的数据复杂度达到约99.4%的攻击成功率。
In order to further study the application of the neural network in cryptanalysis,the neural network differential divider of several typical lightweight block cipher algorithms is constructed and improved by using a deep residual network and traditional differential cryptanalysis techniques.The main results are as follows.First,the neural distinguishers of 4 to 7 rounds of PRESENT,3 rounds of KLEIN,7 to 9 rounds of LBlock and 7 to 10 rounds of Simeck32/64 are constructed and analyzed respectively based on the block cipher structure.Second,based on the characteristics of SPN structure block ciphers,PRESENT and KLEIN′s neural distinguishers are improved,which can improve the accuracy of about 5.12%at most.In the study of LBlock’s neural distinguisher,it is verified that this improved method is not suitable for Feistel structure block ciphers.Third,based on the characteristics of the simeck 32/64 cryptography algorithm,the neural distinguisher is improved,with the accuracy improved by 2.3%.Meanwhile,the improved method of Simeck 32/64 is combined with the polyhedral difference analysis,and the accuracy of the existing 8-round and 9-round Simeck 32/64 poly neural network difference partition is increased by 1%and 3.2%.Finally,the three types of neural distinguishers obtained in the experiment are applied to the last round key recovery attack of 11-round simeck 32/64,with the best experimental result being a 99.4%success rate with 26.6 data complexity in 1000 attacks.
作者
杨小雪
陈杰
YANG Xiaoxue;CHEN Jie(School of Telecommunications Engineering,Xidian University,Xi’an 710071,China;Henan Key Laboratory of Network Cryptography Technology,Zhengzhou 450001,China)
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2024年第1期210-222,共13页
Journal of Xidian University
基金
陕西省自然科学基础研究计划(2021JM-126)
河南省网络密码技术重点实验室研究课题(LNCT2022-A08)。
作者简介
杨小雪(1997-),女,西安电子科技大学硕士研究生,E-mail:1500020789@qq.com;通信作者:陈杰(1979-),女,副教授,E-mail:jchen@mail.xidian.edu.cn。
