Sequence Cipher Based Machine Learning-Attack Resistance Method for Strong-PUF

Pengjun WANG; Jiana LIAN; Bo CHEN

doi:10.11999/JEIT210726

Volume 43 Issue 9

Sep. 2021

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(9): 2474-2481

Pengjun WANG, Jiana LIAN, Bo CHEN. 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

Citation:

Pengjun WANG, Jiana LIAN, Bo CHEN. 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

Citation:

PDF( 5538 KB)

Sequence Cipher Based Machine Learning-Attack Resistance Method for Strong-PUF

doi: 10.11999/JEIT210726

Pengjun WANG^{1
,
,},
Jiana LIAN^{1, 2},
Bo CHEN¹

1.
College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China
2.
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China

Funds: The National Key Research and Development Program of China (2018YFB2202100), The National Natural Science Foundation of China (62174121, 61904125), The Wenzhou Basic Scientific Research Projects (G20190006, G20210023)

Received Date: 2021-07-19
Rev Recd Date: 2021-08-20

Available Online: 2021-09-06

Publish Date: 2021-09-16

Abstract

Abstract

Physical Unclonable Function (PUF) has extremely important application prospects to the field of information security, however, there are also shortcomings in its own security from machine learning attacks and other aspects. By studying PUF circuits and cryptographic algorithm, a method based on sequence cipher of strong-PUF is proposed to resist machine learning attacks. Firstly, the random key is generated by constructing a rolling key generator, which is obfuscated with the input challenge; Then the obfuscated challenge is applied to the strong-PUF through a series-parallel conversion circuit to generate the output response; Finally, Python software simulation and FPGA hardware implementation are used to analyze the safety and statistical properties. The experimental results show that the attack prediction rates based on logistic regression, artificial neural network and support vector machine are close to the ideal value of 50% when the CRPs used for modeling are up to 10⁶ groups. In addition, this method has high versatile, low hardware overhead and does not affect the randomness, uniqueness and reliability of PUF.
- Hardware security,
- Strong-Physical Unclonable Function (PUF),
- Sequence cipher,
- Machine learning

FullText(HTML)

References(18)

References

[1]	汪鹏君, 李乐薇, 郑雁公, 等. 基于气敏传感器的高稳态物理不可克隆函数发生器[J]. 电子与信息学报, 2021, 43(6): 1596–1602. doi: 10.11999/JEIT201104 WANG Pengjun, LI Lewei, ZHENG Yangong, et al. High steady-state physical unclonable function generator based on gas sensors[J]. Journal of Electronics &Information Technology, 2021, 43(6): 1596–1602. doi: 10.11999/JEIT201104
[2]	ZHANG Jiliang and QU Gang. Physical unclonable function-based key sharing via machine learning for IoT security[J]. IEEE Transactions on Industrial Electronics, 2020, 67(8): 7025–7033. doi: 10.1109/TIE.2019.2938462
[3]	AMAN M N, TANEJA S, SIKDAR B, et al. Token-based security for the internet of things with dynamic energy-quality tradeoff[J]. IEEE Internet of Things Journal, 2019, 6(2): 2843–2859. doi: 10.1109/JIOT.2018.2875472
[4]	CHATTERJEE B, DAS D, MAITY S, et al. RF-PUF: Enhancing IoT security through authentication of wireless nodes using in-situ machine learning[J]. IEEE Internet of Things Journal, 2019, 6(1): 388–398. doi: 10.1109/JIOT.2018.2849324
[5]	PAPPU R, RECHT B, TAYLOR J, et al. Physical one-way functions[J]. Science, 2002, 297(5589): 2026–2030. doi: 10.1126/science.1074376
[6]	LI Gang, WANG Pengjun, MA Xuejiao, et al. A 215-F² bistable physically unclonable function with an ACF of < 0.005 and a native bit instability of 2.05% in 65-nm CMOS process[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2020, 28(11): 2290–2299. doi: 10.1109/TVLSI.2020.3014892
[7]	CUI Yijun, WANG Chenghua, LIU Weiqiang, et al. Lightweight configurable ring oscillator PUF based on RRAM/CMOS hybrid circuits[J]. IEEE Open Journal of Nanotechnology, 2020, 1: 128–134. doi: 10.1109/OJNANO.2020.3040787
[8]	LIU Weiqiang, ZHANG Lei, ZHANG Zhengran, et al. XOR-based low-cost reconfigurable PUFs for IoT security[J]. ACM Transactions on Embedded Computing Systems, 2019, 18(3): 25. doi: 10.1145/3274666
[9]	徐金甫, 吴缙, 李军伟, 等. 基于敏感度混淆机制的控制型物理不可克隆函数研究[J]. 电子与信息学报, 2019, 41(7): 1601–1609. doi: 10.11999/JEIT180775 XU Jinfu, WU Jin, LI Junwei, et al. Controlled physical unclonable function research based on sensitivity confusion mechanism[J]. Journal of Electronics &Information Technology, 2019, 41(7): 1601–1609. doi: 10.11999/JEIT180775
[10]	ZHANG Jiliang and SHEN Chaoqun. Set-based obfuscation for strong PUFs against machine learning attacks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2021, 68(1): 288–300. doi: 10.1109/TCSI.2020.3028508
[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]	GAO Yansong, MA Hua, AL-SARAWI S F, et al. PUF-FSM: A controlled strong PUF[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018, 37(5): 1104–1108. doi: 10.1109/TCAD.2017.2740297
[13]	刘伟强, 崔益军, 王成华. 一种低成本物理不可克隆函数结构的设计实现及其RFID应用[J]. 电子学报, 2016, 44(7): 1772–1776. doi: 10.3969/j.issn.0372-2112.2016.07.036 LIU Weiqiang, CUI Yijun, and WANG Chenghua. Design and implementation of a low-cost physical unclonable function and its application in RFID[J]. Acta Electronica Sinica, 2016, 44(7): 1772–1776. doi: 10.3969/j.issn.0372-2112.2016.07.036
[14]	SANTIKELLUR P and CHAKRABORTY R S. A computationally efficient tensor regression network-based modeling attack on XOR arbiter PUF and its variants[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021, 40(6): 1197–1206. doi: 10.1109/TCAD.2020.3032624
[15]	VIJAYAKUMAR A and KUNDU S. A novel modeling attack resistant PUF design based on non-linear voltage transfer characteristics[C]. Proceedings of 2015 Design, Automation & Test in Europe Conference & Exhibition, Grenoble, France, 2015: 653–658. doi: 10.7873/DATE.2015.0522.
[16]	AVVARU S V S and PARHI K K. Effect of loop positions on reliability and attack resistance of feed-forward PUFs[C]. Proceedings of 2019 IEEE Computer Society Annual Symposium on VLSI, Miami, USA, 2019: 366–371. doi: 10.1109/ISVLSI.2019.00073.
[17]	XU Yunhao, LAO Yingjie, LIU Weiqiang, et al. Mathematical modeling analysis of strong physical unclonable functions[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020, 39(12): 4426–4438. doi: 10.1109/TCAD.2020.2969645
[18]	李俊志, 关杰. 非线性反馈移存器型序列密码的完全性通用算法[J]. 电子学报, 2018, 46(9): 2075–2080. doi: 10.3969/j.issn.0372-2112.2018.09.005 LI Junzhi and GUAN Jie. Universal algorithm of full diffusion of stream cipher based on nonlinear feedback shift register[J]. Acta Electronica Sinica, 2018, 46(9): 2075–2080. doi: 10.3969/j.issn.0372-2112.2018.09.005