改进Simon32/64和Simeck32/64神经网络差分区分器  

Improved Neural Network Differential Distinguisher of Simon32/64 and Simeck32/64

作　　者：武浩莹 陈杰[1,2] 刘君 WU Haoying;CHEN Jie;LIU Jun(School of Telecommunications Engineering,Xidian University,Xi’an 710071,China;Henan Key Laboratory of Network Cryptography Technology,Zhengzhou 450001,China;School of Computer Science,Shaanxi Normal University,Xi’an 710119,China)

机构地区：[1]西安电子科技大学通信工程学院,西安710071 [2]河南省网络密码技术重点实验室,郑州450001 [3]陕西师范大学计算机科学学院,西安710119

出　　处：《信息网络安全》2025年第2期249-259,共11页Netinfo Security

基　　金：国家自然科学基金[62302285];河南省网络密码技术重点实验室研究课题[LNCT2022-A08]。

摘　　要：神经网络差分区分器具备良好的泛化能力和强大的学习能力,但目前仍缺乏完善且具有普适性的神经网络差分区分模型。为提升Simon32/64和Simeck32/64神经网络差分区分器的准确率和普适性,文章提出3个改进方向。首先,采用多密文对作为Simon32/64和Simeck32/64的输入,并将Inception网络模块引入神经网络模型,以改善过拟合现象。然后,将Simon32/64和Simeck32/64倒数第二轮的差分信息加入多密文对输入样本中,构造7~10轮和7~11轮神经网络差分区分器。最后,将多密文对与多面体差分结合,根据Simon32/64和Simeck32/64两种密码构造改进多面体差分区分器,提高已有多面体神经网络差分区分器的准确率。实验结果表明,8轮Simon32/64和Simeck32/64新型多面体神经网络差分区分器的准确率分别达到99.54%和99.67%。此外,利用10轮神经网络差分区分器对12轮Simon32/64和Simeck32/64开展最后一轮子密钥恢复攻击,在100次攻击实验中,攻击成功率分别达到86%和97%。Neural distinguishers have good generalisation ability as well as powerful learning ability,but there is still a lack of perfect and universal neural network distinguishing model.In order to increase the accuracy of the neural distinguishers of Simon32/64 and Simeck32/64,increase the generalizability of the neural differential distinguishers,this paper proposed three improvement directions.First,multiple ciphertext pairs were used as inputs to the Simon32/64 and Simeck32/64 neural distinguishers,and the Inception network module was added to the neural network model to improve the overfitting phenomenon.Then,added Simon32/64 and Simeck32/64 penultimate round difference information to the multi-ciphertext pair input samples,constructed the netural distinguishers of 7 to 10 rounds of Simon32/64 and 7 to 11 rounds of Simeck32/64.Finelly,multiple ciphertext pairs were combined with polyhedral difference,constructed polyhedral differential distinguishers for Simon32/64 and Simeck32/64.The accuracy of the polyhedral neural distinguishers were improved.The experimental results show that the new polyhedral netural distinguishers of 8-round of Simon32/64 reach the accuracy of 99.54%and 8-round of Simeck32/64 reach the accuracy of 99.67%.In addition,the improved netural distinguishers of the 10-round of Simon32/64 and Simeck32/64 are applied to the final round of key recovery attacks of 12-round of Simon32/64 and Simeck32/64,the success rate of the attacks respectively reaches 86%and 97%in 100 attack experiments.

关 键 词：深度学习 Inception模块 多密文对 多面体差分 密钥恢复攻击 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]