基于流形学习能量数据预处理的模板攻击优化方法  

An Improved Template Analysis Method Based on Power Traces Preprocessing with Manifold Learning

作　　者：袁庆军 王安 王永娟[1,2] 王涛 YUAN Qingjun;WANG An;WANG Yongjuan;WANG Tao(PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China;Henan Key Laboratory of Network Cryptography Technology,Zhengzhou 450001,China;School of Computer Science,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]战略支援部队信息工程大学,郑州450001 [2]河南省网络密码技术重点实验室,郑州450001 [3]北京理工大学计算机学院,北京100081

出　　处：《电子与信息学报》2020年第8期1853-1861,共9页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61872040);河南省网络密码技术重点实验室开放基金(LNCT2019-S02);“十三五”国家密码发展基金(MMJJ20170201)。

摘　　要：能量数据作为模板攻击过程中的关键对象,具有维度高、有效维度少、不对齐的特点,在进行有效的预处理之前,模板攻击难以奏效。针对能量数据的特性,该文提出一种基于流形学习思想进行整体对齐的方法,以保留能量数据的变化特征,随后通过线性投影的方法降低数据的维度。使用该方法在Panda 2018 challenge1标准数据集进行了验证,实验结果表明,该方法的特征提取效果优于传统的PCA和LDA方法,能大幅度提高模板攻击的成功率。最后采用模板攻击恢复密钥,仅使用两条能量迹密钥恢复成功率即可达到80%以上。As the key object in the process of template analysis,power traces have the characteristics of high dimension,less effective dimension and unaligned.Before effective preprocessing,template attack is difficult to work.Based on the characteristics of energy data,a global alignment method based on manifold learning is proposed to preserve the changing characteristics of power traces,and then the dimensionality of data is reduced by linear projection.The method is validated in Panda 2018 challenge1 standard datasets respectively.The experimental results show that the feature extraction effect of this method is superior over that of traditional PCA and LDA methods.Finally,the method of template analysis is used to recover the key,and the recovery success rates can reach 80%with only two traces.

关 键 词：信息安全 模板攻击 流形学习 能量数据 对齐算法 降维算法 

分 类 号：TP309.7[自动化与计算机技术—计算机系统结构]