摘要
旁路建模分析方法可以有效攻击密码实现,其中基于卷积神经网络的旁路密码分析方法(CNNSCA)可以高效地进行密码攻击,甚至能够攻击有防护的加密算法设备。针对现阶段旁路密码分析建模方法的研究现状,对比分析了几种CNNSCA的模型特点和性能差异,并针对典型CNN模型结构以及旁路信号公共数据集ASCAD,通过模型对比及实验结果分析不同的CNN网络建模方法的效果,进而分析影响CNNSCA方法的性能因素、基于卷积神经网络的旁路建模方法的优势。由分析可知,基于VGG变体的CNNSCA在攻击各种情况的目标数据集时泛化性、鲁棒性表现最好,但使用的CNN模型训练程度及超参数设置是否最适用于SCA场景并未得到验证。今后研究者可通过调整CNN模型的各种超参数,使用数据增强技术,结合Imagenet大赛中优秀CNN网络等手段,来提升CNNSCA的分类准确率和破密性能,探索最适用于SCA场景的CNN模型是未来的发展趋势。
The profiled side-channel analysis method can effectively attack the implementation of cryptographic,and the side-channel cryptanalysis method based on convolutional neural network(CNNSCA)can efficiently carry out cryptographic attacks,and even can attack the implementation of protected encryption algorithms.In view of the current research status of side-channel cryptanalysis profiling methods,this paper compares and analyzes the characteristics and performance differences of several CNNSCA models,and focuses on the typical CNN model structure and side-channel signal public data set ASCAD.Through model comparison and experimental results,it compares and analyzes the effects of different CNN network modeling methods,and then analyzes the performance factors that affect the CNNSCA method and the advantages of the side-channel profiling method based on convolutional neural networks.Research and analysis show that CNNSCA based on VGG variants performs best in generalization and robustness when attacking target data sets in various situations,but whether the training level of the used CNN model and the hyperparameter settings are most suitable for SCA scenarios have not been verified.In the future,researchers can improve the classification accuracy and decryption performance of CNNSCA by adjusting various hyperparameters of the CNN model,use data enhancement techniques and combine the excellent CNN network in the Imagenet competition to explore the most suitable CNN model for SCA scenarios,which is a development trend.
作者
刘林云
陈开颜
李雄伟
张阳
谢方方
LIU Lin-yun;CHEN Kai-yan;LI Xiong-wei;ZHANG Yang;XIE Fang-fang(Center of Equipment Simulation Training,Shijiazhuang Campus of the Army Engineering University,Shijiazhuang 050003,China)
出处
《计算机科学》
CSCD
北大核心
2022年第5期296-302,共7页
Computer Science
基金
国家自然科学基金(51377170,61602505)。
关键词
旁路分析
建模方法
卷积神经网络
超参数
性能评估
Side-channel analysis
Profiling method
Convolutional neural network
Hyperparameter
Performance evaluation
