数学工程与先进计算国家重点实验室开放基金(2018A03); 国家密码发展基金(MMJJ20180203); 信息保障技术重点实验室开放基金(KJ-17-002)
差分分析在分组密码分析领域是一种重要的研究方法, 针对分组密码的差分分析的重点在于找到一个轮数或者概率更大的差分区分器. 首先描述了通过深度学习技术构造差分区分器时所需要的数据集的构造方法, 并且分别基于卷积神经网络(convolutional neural networks, CNN)和残差神经网络(residual neural network, ResNet)训练了两种轻量级分组密码算法SIMON32与SPECK32的差分区分器, 并对两种模型得到的差分区分器进行了比较, 发现综合考虑时间花销与精度的前提下, 在SIMON32的差分区分器构造上, ResNet训练得到的模型表现更好, 而CNN则在SPECK32的模型训练上表现的更好; 其次, 研究了网络模型中卷积运算个数对模型精度的影响, 发现在原有模型基础上增加CNN模型的卷积层数和ResNet模型的残差块数, 都会导致模型精度的下降. 最后, 给出在进行基于深度学习的差分区分器构造时的模型及参数选择建议, 即, 应该首要考虑低卷积层数的CNN模型和低残差块数的ResNet模型.
Differential cryptanalysis is an important method in the field of block cipher. The key point of differential cryptanalysis is to find a differential distinguisher with longer rounds or higher probability. Firstly, the method of generating data set is described which is used to train a differential distinguisher based on deep learning. At the same time, the differential distinguisher of two kinds of lightweight block cipher is trained, SIMON32 and SPECK32, based on convolutional neural networks (CNN) and residual neural network (ResNet). In addition, two differential distinguishers are compared and it is found that ResNet is good at differential distinguisher of SIMON32, CNN is good at SPECK32 when considering time and accuracy. Next, the influence of the number of convolution operations of the network model is studied on the accuracy of the neural distinguisher, and it is found that adding the number of convolution layers of the CNN and the number of residual blocks of the ResNet model will cause the accuracy decrease compared with original networks. Finally, some suggestions are given to select networks and parameters when constructing a differential distinguisher based on deep learning, i.e., the CNN with low convolutional layers and the ResNet with low residual blocks should be considered as the first choose.