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基于混沌映射的抗机器学习攻击强物理不可克隆函数

汪鹏君 方皓冉 李刚

汪鹏君, 方皓冉, 李刚. 基于混沌映射的抗机器学习攻击强物理不可克隆函数[J]. 电子与信息学报, 2024, 46(5): 2281-2288. doi: 10.11999/JEIT231129
引用本文: 汪鹏君, 方皓冉, 李刚. 基于混沌映射的抗机器学习攻击强物理不可克隆函数[J]. 电子与信息学报, 2024, 46(5): 2281-2288. doi: 10.11999/JEIT231129
WANG Pengjun, FANG Haoran, LI Gang. Design of Strong Physical Unclonable Function Circuit Against Machine Learning Attacks Based on Chaos Mapping[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2281-2288. doi: 10.11999/JEIT231129
Citation: WANG Pengjun, FANG Haoran, LI Gang. Design of Strong Physical Unclonable Function Circuit Against Machine Learning Attacks Based on Chaos Mapping[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2281-2288. doi: 10.11999/JEIT231129

基于混沌映射的抗机器学习攻击强物理不可克隆函数

doi: 10.11999/JEIT231129
基金项目: 国家自然科学基金(62234008, 62374117),温州市基础性科研项目(G20220005)
详细信息
    作者简介:

    汪鹏君:男,教授,研究方向为集成电路设计、信息安全等技术及其相关理论

    方皓冉:男,硕士生,研究方向为物理不可克隆函数的攻击与防御

    李刚:男,副教授,研究方向为集成电路设计、硬件安全

    通讯作者:

    汪鹏君 wangpengjun@wzu.edu.cn

  • 中图分类号: TN4; TP309

Design of Strong Physical Unclonable Function Circuit Against Machine Learning Attacks Based on Chaos Mapping

Funds: The National Natural Science Foundation of China (62234008, 62374117), Wenzhou Basic Scientific Research Projects (G20220005)
  • 摘要: 物理不可克隆函数(PUF)在硬件安全领域具有广阔的应用前景,然而易受到基于机器学习等建模攻击。通过对强PUF电路结构和混沌映射机理的研究,该文提出一种可有效抵御机器学习建模攻击的PUF电路。该电路将原始激励作为混沌映射初始值,利用PUF激励响应映射时间与混沌算法迭代深度之间的内在联系产生不可预测的混沌值,并采用PUF中间响应反馈加密激励,进一步提升激励与响应映射的复杂度,增强PUF的抗机器学习攻击能力。该PUF采用Artix-7 FPGA实现,测试结果表明,即使选用的激励响应对数量高达106组,基于逻辑回归、支持向量机和人工神经网络的攻击预测率仍接近50%的理想值,并具有良好的随机性、唯一性和稳定性。
  • 图  1  APUF电路结构

    图  2  Logistic映射(虫口模型)

    图  3  CM-PUF原理框图

    图  4  CM-PUF电路结构

    图  5  测试平台与测试系统

    图  6  机器学习攻击预测率

    图  7  自相关性测试结果

    图  8  片间汉明距离和片内汉明距离拟合曲线

    表  1  CM-PUF 的随机性NIST 测试结果

    NIST检测项APUF5XOR-PUFCM-PUF
    P-valueProportionP-valueProportionP-valueProportion
    频率检验0*4/500*0/500*39/50
    场内频数检验0*0/500*1/500.026 94850/50
    游程检验0*0/500*0/500*28/50
    块内最长游程检验0*0/500*0/500*36/50
    2元矩阵秩检验0.574 90347/500.425 30550/500.236 81050/50
    离散傅里叶变换检验0*0/500*0/500.108 79150/50
    非重叠模块匹配检验0*37/500.322 34650/500.991 46850/50
    重叠模块匹配检验0*11/500*19/500.383 82749/50
    线性复杂度检验0.236 81049/500.419 02149/500.779 18848/50
    累积和检验0*0/500*0/500*40/50
    通用统计检验0*0/500*0/500*50/50
    近似熵检验0*0/500*0/500.013 56946/50
    序列检验0*0/500*0/500*26/50
    *表示P-value 的值非常小,接近于0
    下载: 导出CSV

    表  2  各类PUF电路统计特性、抗攻击能力与硬件开销对比

    PUF类型 随机性(%) 唯一性(%) 稳定性(%) 机器学习攻击预测率(%) 硬件开销
    LR ANN SVM LUTs FFs SLICEs
    APUF[1] 50.73 49.56 99.02 99.30 99.12 98.98 266 278 109
    2XOR-PUF[1] 50.13 49.35 98.36 96.91 96.63 96.74 469 279 177
    5XOR-PUF[1] 50.07 50.59 92.54 53.89 85.47 54.31 786 282 440
    CT PUF[4] 49.00~50.00 49.00~50.00 92.00 57.00 55.00 731 486 244
    CaPUF[6] 46.38 47.03 88.47 50.00~56.00 50.00~56.00 5723
    CM-PUF 50.53 50.08 98.74 50.33 50.33 50.24 491 546 240
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-17
  • 修回日期:  2024-01-21
  • 网络出版日期:  2024-01-29
  • 刊出日期:  2024-05-30

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