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用于检测硬件木马延时的线性判别分析算法

宋钛 黄正峰 徐辉

宋钛, 黄正峰, 徐辉. 用于检测硬件木马延时的线性判别分析算法[J]. 电子与信息学报, 2023, 45(1): 59-67. doi: 10.11999/JEIT220389
引用本文: 宋钛, 黄正峰, 徐辉. 用于检测硬件木马延时的线性判别分析算法[J]. 电子与信息学报, 2023, 45(1): 59-67. doi: 10.11999/JEIT220389
SONG Tai, HUANG Zhengfeng, XU Hui. Linear Discriminant Analysis Algorithm for Detecting Hardware Trojans Delay[J]. Journal of Electronics & Information Technology, 2023, 45(1): 59-67. doi: 10.11999/JEIT220389
Citation: SONG Tai, HUANG Zhengfeng, XU Hui. Linear Discriminant Analysis Algorithm for Detecting Hardware Trojans Delay[J]. Journal of Electronics & Information Technology, 2023, 45(1): 59-67. doi: 10.11999/JEIT220389

用于检测硬件木马延时的线性判别分析算法

doi: 10.11999/JEIT220389
基金项目: 国家自然科学基金(61874156, 62174001),安徽省基金(202104b11020032, 2208085J02)
详细信息
    作者简介:

    宋钛:男,博士,讲师,主要研究方向为集成电路测试和硬件安全

    黄正峰:男,博士,教授,博士生导师,主要研究方向为容错计算

    徐辉:男,教授,硕士生导师,主要研究方向为集成电路设计与老化测试

    通讯作者:

    黄正峰 huangzhengfeng@139.com

  • 中图分类号: TN406; TN710

Linear Discriminant Analysis Algorithm for Detecting Hardware Trojans Delay

Funds: The National Natural Science Foundation of China (61874156, 62174001), Anhui Province Foundation (202104b11020032, 2208085J02)
  • 摘要: 针对芯片生产链长、安全性差、可靠性低,导致硬件木马防不胜防的问题,该文提出一种针对旁路信号分析的木马检测方法。首先采集不同电压下电路的延时信号,通过线性判别分析(LDA)分类算法找出延时差异,若延时与干净电路相同,则判定为干净电路,否则判定有木马。然后联合多项式回归算法对木马延时特征进行拟合,基于回归函数建立木马特征库,最终实现硬件木马的准确识别。实验结果表明,提出的LDA联合线性回归(LR)算法可以根据延时特征识别木马电路,其木马检测率优于其他木马检测方法。更有利的是,随着电路规模的增大意味着数据量的增加,这更便于进行数据分析与特征提取,降低了木马检测难度。通过该方法的研究,对未来工艺极限下识别木马电路、提高芯片安全性与可靠性具有重要的指导作用。
  • 图  1  硬件木马结构

    图  2  延时信号分析

    图  3  LDA分类算法

    图  4  线性回归方程

    图  5  实验设置

    图  6  实验流程

    图  7  不同电压下木马电路与干净电路延时对比

    图  8  干净电路与木马电路线性拟合对比

    图  9  温度和电压校准设置

    图  10  不同温度和电压下检测到的木马数量

    算法1 LDA 算法
     输入:带标签的原始向量
     输出:矩阵积
      循环执行如下操作
      1. 计算数据集中不同类别的多维平均向量。
      2. 计算矩阵(类间和类内矩阵)。
      3. 计算矩阵的特征向量和相应的特征值。
      4. 对特征向量进行递减特征值排序,选择特征值最大的K个特
       征向量,形成D×K维矩阵W
      5. 使用这个 D×K 特征向量矩阵将模式转换到新的子空间。
      6. 对矩阵求积:Y=X×W(其中X是表示n个模式的n×D维矩
       阵,Y是新子空间中变换后的n×k维模式)。
     结束
    下载: 导出CSV
    算法2 线性回归算法
     输入:训练数据集和样本特征向量
     输出:计算多项式的值 y=w·xb
     对于每一个参数wb执行如下操作
      1. 前向传递:计算损失函数 L
      2. 向后传递:计算相对于 w 的梯度 G1的损失函数
      3. 根据G1更新参数
      4. 惩罚项:计算相对于w的梯度G2的损失函数
      5. 根据G2更新参数
     结束
    下载: 导出CSV

    表  1  测试台数据

    基准电路门数量电路面积(μm2)功率(W)
    AES10610163×1630.0732
    MIPS8661195×1950.0494
    RS Decoder23224394×3940.12
    JPEG Encoder2699701094×10941.4675
    下载: 导出CSV

    表  2  本文识别率与以往技术的比较(%)

    基准电路LTPD
    (μW/μm2)
    GNJ[16]BSC[17]LPA[18]ML[19]本文方法
    AES0.1488.0 2.02.22.73.0
    0.22326.519.115.416.3431.4
    0.27931.243.5 54.467.286.5
    0.36964.977.689.986.796.7
    0.44390.896.598.599.5100
    MIPS0.1545.68.011.719.732.5
    0.23110.816.523.339.560.6
    0.30817.726.040.162.681.8
    0.38427.639.458.277.891.1
    0.46339.355.973.788.592.4
    RS Decoder0.0623.74.84.37.59.8
    0.0923.74.84.37.59.8
    0.1224.87.39.113.421.2
    0.1566.19.312.522.236.2
    0.21810.112.820.938.157
    JPEG Encoder0.0041.52.02.03.13.2
    0.0153.37.57.88.08.0
    0.0264.510.511.113.015.0
    0.03710.315.520.621.225.8
    0.04811.118.530.345.650.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-02
  • 修回日期:  2022-06-29
  • 网络出版日期:  2022-07-21
  • 刊出日期:  2023-01-17

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