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基于流形学习能量数据预处理的模板攻击优化方法

袁庆军 王安 王永娟 王涛

引用本文: 袁庆军, 王安, 王永娟, 王涛. 基于流形学习能量数据预处理的模板攻击优化方法[J]. 电子与信息学报, doi: 10.11999/JEIT190598 shu
Citation:  Qingjun YUAN, An WANG, Yongjuan WANG, Tao WANG. An Improved Template Analysis Method Based on Power Traces Preprocessing with Manifold Learning[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190598 shu

基于流形学习能量数据预处理的模板攻击优化方法

    作者简介: 袁庆军: 男,1993年生,助教教授,研究方向为机器学习侧信道分析;
    王安: 男,1983年生,副教授,研究方向为侧信道分析与防护技术;
    王永娟: 女,1982年生,研究员,研究方向为侧信道分析与密码系统安全;
    王涛: 男,1995年生,硕士生,研究方向为机器学习侧信道分析
    通讯作者: 王永娟,pinkywyj@163.com
  • 基金项目: 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。

English

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  • 图 1  PANDA 2018 Challenge1 前3条能量迹

    图 2  PANDA 2018 Challenge1 能量迹与密钥相关系数

    图 3  PANDA 2018 Challenge1能量数据对齐后

    图 4  PANDA 2018 Challenge1 能量迹降维后

    图 5  PANDA 2018 Challenge1 能量迹PCA-20或LDA-20降维后

    表 1  向量矩阵计算算法

     输入:能量数据${T_\alpha } = {\rm{\{ } }{T_i},0 \le i \le \alpha ,i \in N\}$,对齐参数$k$。
     输出:对齐后的能量数据${T'_\alpha }$
     (1) for j in range(α), do
     (2)  计算与${T_j}$ 欧式距离最近的$k$条能量迹${\rm{\{ }}{T_{j1}},{T_{j2}}, ··· ,{T_{jk}}\} $;
     (3) end
     (4) for j in range (α), do
     (5)  计算关系向量矩阵${{{W}}_{{j}}} = \frac{{\left( {{{C}}_i^{ - 1} \cdot {{{1}}_k}} \right)}}{{{{1}}_k^T \cdot {{C}}_i^{ - 1} \cdot {{{1}}_k}}}$,其中${C_i}$为
        ${\rm{\{ }}{T_{j1}},{T_{j2}}, ··· ,{T_{jk}}\} $的协方差矩阵,${{{1}}_k}$为$k$维全1向量;
     (6) end
     (7) 计算矩阵${{M}} = ({{{\rm I}}} - {{W}}){({{I}} - {{W}})^{\rm{T}}}$;
     (8) 设$\beta = \alpha /2$从矩阵M中选择较小的$\beta $个特征值,记为${{{M}}_\beta }$,
        计算${T'_\alpha } = T \cdot {{{M}}_\beta }$;
     (9) return ${T_\alpha }^\prime $
    下载: 导出CSV

    表 2  PANDA 2018 Challenge1数据集预处理后方差表(汉明重量不同)

    方差0137153163127255
    04.0810.9914.3116.619.8015.8018.3213.0210.19
    110.992.6712.498.837.349.5011.485.006.33
    314.3112.498.5313.6215.2112.6711.7313.0015.81
    716.618.8313.623.6216.248.1311.604.9910.73
    159.807.3415.2116.244.2312.2112.859.239.84
    3115.809.5012.678.1312.214.1711.628.869.61
    6318.3211.4811.7311.6012.8511.624.549.269.73
    12713.025.0013.004.999.238.869.261.975.23
    25510.196.3315.8110.739.849.619.735.234.26
    下载: 导出CSV

    表 3  PANDA 2018 Challenge1数据集预处理后方差表(汉明重量相同)(单位:万)

    方差7111314193567131224
    73.6211.2323.7012.1913.3513.5211.5514.049.86
    1111.232.6018.8011.7312.0711.8512.4310.9710.21
    1323.7018.8031.9123.0427.0922.5223.5856.3319.22
    1412.1911.7323.043.8912.549.5214.4714.9612.70
    1913.3512.0727.0912.544.7813.8615.3317.6811.98
    3513.5211.8522.529.5213.863.1515.0715.1010.67
    6711.5512.4323.5814.4715.3315.074.9817.739.50
    13114.0410.9756.3314.9617.6815.1017.7337.0420.31
    2249.8610.2119.2212.7011.9810.679.5020.313.91
    下载: 导出CSV

    表 4  PANDA 2018 Challenge1数据集PCA-20处理后方差表(汉明重量不同)(单位:万)

    方差0137153163127255
    033.0027.9730.5829.5828.9630.9129.0731.0431.06
    127.9713.7215.9716.0515.2316.1015.9920.4914.26
    330.5815.9713.7916.9715.9717.5715.5823.6016.56
    729.5816.0516.9717.0416.7017.6017.3422.6517.31
    1528.9615.2315.9716.7014.5316.8316.0721.6016.43
    3130.9116.1017.5717.6016.8316.6416.6522.5717.06
    6329.0715.9915.5817.3416.0716.6515.4122.2716.76
    12731.0420.4923.6022.6521.6022.5722.2724.3622.35
    25531.0614.2616.5617.3116.4317.0616.7622.3513.91
    下载: 导出CSV

    表 5  PANDA 2018 Challenge1数据集LDA-20处理后方差表(汉明重量不同)(单位:万)

    方差0137153163127255
    00.951.210.930.991.071.091.081.121.13
    11.211.131.071.171.201.111.241.151.20
    30.931.070.650.900.990.931.001.051.01
    70.991.170.900.840.971.021.101.091.06
    151.071.200.990.970.921.081.171.161.11
    311.091.110.931.021.080.891.101.101.02
    631.081.241.001.101.171.101.071.181.15
    1271.121.151.051.091.161.101.180.981.15
    2551.131.201.011.061.111.021.151.150.97
    下载: 导出CSV
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文章相关
  • 通讯作者:  王永娟, pinkywyj@163.com
  • 收稿日期:  2019-08-07
  • 录用日期:  2019-10-31
  • 网络出版日期:  2019-11-27
通讯作者: 陈斌, bchen63@163.com
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