基于路径敏化的多熵源软PUF

doi:10.19665/j.issn1001-2400.2022.06.008

摘要/Abstract

摘要：

物理不可克隆函数作为一种芯片指纹,已经在信息安全领域获得了广泛应用。但是,目前主流物理不可克隆函数需要设计独特的硬件结构以获取特征信息,在极端开销受限系统方面的应用面临着巨大的挑战。故以路径敏化为研究对象,结合器件延迟偏差特性与寄存器采样不确定性,提出一种从已有硬件结构中提取偏差数据的多熵源软物理不可克隆函数设计方案。该方案首先选择若干组测试激励敏化目标路径,建立物理不可克隆函数响应与芯片特征的映射关系;然后分别在电路网表中插入扫描链结构,在触发器采样阶段施加不同超频时钟信号,提取芯片的异常数据;最后将其与标准输出进行对比,统计不同时钟频率下的错误路径条数,并进行随机组合获取物理不可克隆函数响应。实验结果表明,所提物理不可克隆函数惟一性为47.58%,随机性为49.7%,且具有抗机器学习攻击的能力。

关键词: 软物理不可克隆函数, 路径敏化, 多熵源, 低硬件开销, 扫描链

Abstract:

The Physical Unclonable Function (PUF),as a chip fingerprint,has been widely used in the field of information security.However,the current mainstream PUF designs need to add additional hardware to obtain feature information,and the application in extremely cost-constrained systems faces huge challenges.In this paper,with path sensitization taken as the research object,a software PUF scheme with multiple entropy sources for extracting deviation data from the existing hardware structure is proposed by combining the characteristic of device delay deviation and the uncertainty of register sampling.First,several sets of test patterns are selected to sensitize the target paths and establish the mapping relationship between PUF response and chip feature.Second,the scan chain structure is inserted into the circuit netlist,and different overclocking clock signals are applied in the sampling stage of the trigger to extract the chip abnormal data.Finally,the data is compared with the standard output to count the number of error paths at different clock frequencies,and the PUF response is obtained by random combination of the numbers.Experimental results show that the uniqueness of the proposed PUF is 47.58%,that the randomness is 49.7%,and that the PUF can resist machine learning attacks.

Key words: software physical unclonable function, path sensitization, multiple entropy sources, low hardware overhead, scan chain

中图分类号:

TN402

汪鹏君,陈佳,张跃军,庄友谊,李乐薇,倪力. 基于路径敏化的多熵源软PUF[J]. 西安电子科技大学学报, 2022, 49(6): 58-66.

WANG Pengjun,CHEN Jia,ZHANG Yuejun,ZHUANG Youyi,LI Lewei,NI Li. Software PUF with multiple entropy sources based on path sensitization[J]. Journal of Xidian University, 2022, 49(6): 58-66.

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PUF类型	机器学习算法
PUF类型	LR	MLP
APUF	92.23	94.18
XOR APUF	78.01	62.87
文中	61.21	56.43

文献	类型	工艺	惟一性/%	随机性	额外偏差产生电路
[12]	APUF	Xilinx Artix 7	49.10	50.3%	有
[16]	DScanPUF	Altera Cyclone III	49.90	NIST test	有
[17]	Register PUF	Synopsys EDK32/28nm	47.50	59.9%	有
[18]	处理器PUF	Xilinx Spartan 3E	40.60		无
[19]	同步PUF	Altera Cyclone IV	24.35		有
文中	软PUF	TSMC 65 nm CMOS	47.58	49.7%	无