Non-profiled Deep-Learning-Based Power Analysis of the SM4 and DES Algorithms  

作　　者：MA Xiangliang LI Bing WANG Hong WU Di ZHANG Lizhen HUANG Kezhen DUAN Xiaoyi 

机构地区：[1]School of Integrated Circuits,Tsinghua University,Beijing 100084,China [2]National Research Center for Information Technology Security,Beijing 100084,China [3]China Cybersecurity Review Technology and Certification Center,Beijing 100020,China [4]Trusted Computing and Information Assurance Laboratory,Institute of Software,Chinese Academy of Sciences,Beijing 100190,China [5]University of Chinese Academy of Sciences,Beijing 100049,China [6]Department of Electronics and Information Engineering,Beijing Electronic Science and Technology Institute,Beijing 100070,China

出　　处：《Chinese Journal of Electronics》2021年第3期500-507,共8页电子学报（英文版）

基　　金：the National Key Research and Development Program of China(No.2018YFB0904901,No.2019QY1302);National Natural Science Foundation of China(No.61802404).

摘　　要：Power analysis methods are commonly used for evaluating the security of cryptographic devices.They are characteristically low-cost and display a high success rate and the ability to obtain important device information,e.g.,keys.Given the current wide application of deep-learning technology,there is a growing tendency to incorporate power-analysis technology in development.This study investigates non-profiled deep-learning-based power analysis.The labels used in this attack are uncertain,and the attack conditions required are greatly reduced.We choose the Recurrent neural network(RNN),multilayer perceptron,and convolutional neural network algorithms,which use the same network structure,to recover the keys for the SM4 software and DES hardware implementations.We propose combining the RNN algorithm with power analysis,and validate the benefits experimentally.The experimental results show that they all successfully recover the correct key for the SM4 software implementation,although the RNN algorithm by itself achieves a better effect.This conclusion also applies to attacks on the DES hardware implementation but is limited to labels based on the bit model.

关 键 词：Non-profiled deep learning Power analysis SM4 DES Key recovery 

分 类 号：TN918.4[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]