深度学习在分组密码差分区分器上的研究应用

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...

Research and Application of Deep Learning on Differential Distinguisher of Block Cipher

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.