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轻量级可配置强物理不可克隆函数设计

侯申1,2), 李景龙2), 刘海龙2), 李少青1), 郭阳1)
1) (国防科技大学计算机学院 长沙 410073)2) (信息工程大学洛阳校区 洛阳 471003)
分类号: TP309.7 DOI: 10.3724/SP.J.1089.2021.18744
出版年,卷(期):页码: 2021 , 33 ( 10 ): 1627-1634 侯申
摘要: 为了解决物理不可克隆函数(PUF)结构简单、容易遭受建模攻击等问题, 提出一种基于动态线性反馈移位寄存器(LFSR)的强PUF抗攻击混淆设计. 首先使用一个固定结构的LFSR作为伪随机数发生器, 为混淆逻辑提供随机选择信号; 然后使用一个内置多个反馈多项式的动态LFSR作为混淆逻辑, 对输入激励进行混淆; 最后将混淆后的激励输入内嵌PUF电路, 使攻击者无法获取内嵌PUF的真实激励, 从而提高PUF的抗建模攻击能力. 用Python和FPGA进行了仿真和数据收集, 在收集到数据集上的实验表明, 所提设计具有接近理想值的均匀性(49.8%)和唯一性(49.9%), 保持了与经典强PUF相同的可靠性. 该设计结构简单, 硬件开销较低, 能够抵抗多种主流机器学习和深度学习算法的建模攻击.
关键词: 物理不可克隆函数; 仲裁器物理不可克隆函数; 动态线性反馈移位寄存器; 机器学习; 建模攻击
Design of Lightweight and Configurable Strong Physical Unclonable Function
Hou Shen1,2), Li Jinglong2), Liu Hailong2), Li Shaoqing1), and Guo Yang1)
1) (School of Computer, National University of Defense Technology, Changsha 410073)2) (Luoyang Campus, Information Engineering University, Luoyang 471003)
abstract: To solve the problem that the physical unclonable function (PUF) structure is simple and vulnerable to modeling attacks, a strong PUF anti-attack obfuscation design based on linear feedback shift register (LFSR) is proposed. First, a fixed structure LFSR is used as a pseudo-random number generator to provide a random selection signal for the obfuscation logic. Then, a dynamic LFSR with multiple feedback polynomials is used as the obfuscation logic to obfuscate origin challenges. Finally, obfuscated challenges are loaded into the embedded PUF circuit so that the attacker cannot obtain real challenges. It improves the resistance of the PUF to modeling attacks. The proposed design is simulated by Python and FPGA. Experiments on the collected dataset show that the proposed PUF has ideal uniformity (49.8%) and uniqueness (49.9%) and keeps the same reliability. It has simple architecture and low hardware overhead and can resist a variety of modeling attacks including machine learning and deep learning.
keyword: physical unclonable function; arbiter physical unclonable function; dynamic linear feedback shift register; machine learning; modeling attack
 
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