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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. 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. 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. 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. 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), The 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
    LRANNSVMLUTsFFsSLICEs
    APUF[1]50.7349.5699.0299.3099.1298.98266278109
    2XOR-PUF[1]50.1349.3598.3696.9196.6396.74469279177
    5XOR-PUF[1]50.0750.5992.5453.8985.4754.31786282440
    CT PUF[4]49.00~50.0049.00~50.0092.0057.00-55.00731486244
    CaPUF[6]46.3847.0388.4750.00~56.00-50.00~56.005723--
    CM-PUF50.5350.0898.7450.3350.3350.24491546240
    下载: 导出CSV
  • [1] ZHOU Ziyu, LI Gang, and WANG Pengjun. A challenge-screening strategy for enhancing the stability of strong PUF based on machine learning[J]. Microelectronics Journal, 2023, 131: 105667. doi: 10.1016/j.mejo.2022.105667
    [2] 汪鹏君, 连佳娜, 陈博. 基于序列密码的强PUF抗机器学习攻击方法[J]. 电子与信息学报, 2021, 43(9): 2474–2481. doi: 10.11999/JEIT210726

    WANG Pengjun, LIAN Jiana, and CHEN Bo. Sequence cipher based machine learning-attack resistance method for strong-PUF[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2474–2481. doi: 10.11999/JEIT210726
    [3] LI Hui, LI Gang, WANG Pengjun, et al. A novel machine learning attack resistant APUF with dual-edge acquisition[C]. Asian Hardware Oriented Security and Trust Symposium (AsianHOST), Singapore, 2022: 1–4.
    [4] XIE Yuanfeng, LI Gang, WANG Pengjun, et al. A compact weak PUF circuit based on MOSFET subthreshold leakage current[J]. IEICE Electronics Express, 2022, 19(21): 20220415. doi: 10.1587/elex.19.20220415
    [5] ALI R, MA Haoyuan, HOU Zhengyi, et al. A reconfigurable arbiter MPUF with high resistance against machine learning attack[J]. IEEE Transactions on Magnetics, 2021, 57(10): 1–7. doi: 10.1109/TMAG.2021.3102838
    [6] ZHANG Jiliang, SHEN Chaoqun, GUO Zhiyang, et al. CT PUF: Configurable tristate PUF against machine learning attacks for IoT Security[J]. IEEE Internet of Things Journal, 2022, 9(16): 14452–14462. doi: 10.1109/JIOT.2021.3090475
    [7] WU Linjun, HU Yupeng, ZHANG Kehuan, et al. FLAM-PUF: A response–feedback-based lightweight anti-machine-learning-attack PUF[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022, 41(11): 4433–4444. doi: 10.1109/TCAD.2022.3197696
    [8] NASSAR H, BAUER L, and HENKEL J. CaPUF: Cascaded PUF structure for machine learning resiliency[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022, 41(11): 4349–4360. doi: 10.1109/TCAD.2022.3197539
    [9] ZHANG Jieyun, XU Chongyao, LAW M K, et al. A 4T/cell amplifier-chain-based XOR PUF with strong machine learning attack resilience[J]. IEEE Transactions on Circuits and Systems I:Regular Papers, 2022, 69(1): 366–377. doi: 10.1109/TCSI.2021.3114084
    [10] OUN A and NIAMAT M. Design of a delay-based FPGA PUF resistant to machine learning attacks[C]. IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Lansing, USA, 2021: 865–868.
    [11] AVVARU S V S, ZENG Ziqing, and PARHI K K. Homogeneous and heterogeneous feed-forward XOR physical Unclonable functions[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 2485–2498. doi: 10.1109/TIFS.2020.2968113
    [12] SHAH N, CHATTERJEE D, SAPUI B, et al. Introducing recurrence in strong PUFs for enhanced machine learning attack resistance[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2021, 11(2): 319–332. doi: 10.1109/JETCAS.2021.3075767
    [13] WANG Yale, WANG Chenghua, GU Chongyan, et al. A dynamically configurable PUF and dynamic matching authentication protocol[J]. IEEE Transactions on Emerging Topics in Computing, 2022, 10(2): 1091–1104. doi: 10.1109/TETC.2021.3072421
    [14] WANG Yale, WANG Chenghua, GU Chongyan, et al. A generic dynamic responding mechanism and secure authentication protocol for strong PUFs[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2022, 30(9): 1256–1268. doi: 10.1109/TVLSI.2022.3189953
    [15] 张笑天, 汪鹏君, 张跃军, 等. 基于动态亚阈值的延迟型PUF电路设计[J]. 华东理工大学学报:自然科学版, 2022, 48(2): 237–243. doi: 10.14135/j.cnki.1006-3080.20210203001

    ZHANG Xiaotian, WANG Pengjun, ZHANG Yuejun, et al. Design of delayed PUF circuit based on dynamic subthreshold[J]. Journal of East China University of Science and Technology, 2022, 48(2): 237–243. doi: 10.14135/j.cnki.1006-3080.20210203001
    [16] PAN Peng, WANG Haiquan, SHEN Lei, et al. Equivalence of joint ML-decoding and separate MMSE-ML decoding for training-based MIMO systems[J]. IEEE Access, 2019, 7: 178862–178869. doi: 10.1109/ACCESS.2019.2958700
    [17] 杨剑锋, 乔佩蕊, 李永梅, 等. 机器学习分类问题及算法研究综述[J]. 统计与决策, 2019, 35(6): 36–40. doi: 10.13546/j.cnki.tjyjc.2019.06.008

    YANG Jianfeng, QIAO Peirui, LI Yongmei, et al. A review of machine-learning classification and algorithms[J]. Statistics & Decision, 2019, 35(6): 36–40. doi: 10.13546/j.cnki.tjyjc.2019.06.008
    [18] 魏旭晖, 王辉. 异构网络下TCP拥塞控制的混沌特性分析[J]. 系统仿真学报, 2015, 27(7): 1541–1547. doi: 10.16182/j.cnki.joss.2015.07.018

    WEI Xuhui and WANG Hui. Chaos analysis for TCP congestion control in heterogeneous networks[J]. Journal of System Simulation, 2015, 27(7): 1541–1547. doi: 10.16182/j.cnki.joss.2015.07.018
    [19] WANNABOON C and KETTHONG P. A simple random-bit generator implemented on FPGA based on Signum chaotic map[C]. 2022 International Conference on Digital Government Technology and Innovation (DGTi-CON), Bangkok, Thailand, 2022: 101–104.
    [20] LEE C Y, BHARATHI K, LANSFORD J, et al. NIST-lite: Randomness testing of RNGs on an energy-constrained platform[C]. IEEE 39th International Conference on Computer Design (ICCD), Storrs, USA, 2021: 41–48.
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出版历程
  • 收稿日期:  2023-10-17
  • 修回日期:  2024-01-24
  • 网络出版日期:  2024-01-29

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