Year: 2025
Type: article
Abstract: A Physical Unclonable Function (PUF) is a lightweight hardware security primitive with diverse applications in Internet of Things (IoT) security. However, the ongoing advancements in machine learning (ML) technologies have significantly impacted the security of PUFs. This article introduces a dual-state hybrid PUF (DH PUF) that dynamically switches the back-end circuit between a ring oscillator (RO) array and a path selector array based on real-time feedback from a nonlinear front-end circuit. Unlike conventional dual-mode PUFs controlled by static challenges, our design introduces a reverse-order obfuscation mechanism that disrupts the linear correlation between challenges and responses with minimal hardware overhead. The 64-stage DH PUF was implemented on a Xilinx Artix-7 FPGA, and experimental results indicate that the design effectively withstands machine learning attacks. The prediction rates for logistic regression (LR), covariance matrix-adaptive evolutionary strategy (CMA-ES), support vector machine (SVM), and artificial neural network (ANN), deep neural network (DNN) and Multilayer Perceptron (MLP) are all approximately 50%. Additionally, the structure demonstrates a uniqueness of 49.98%, uniformity of 51.32%, and reliability of about 98%.
DOI: 10.1145/3774653
Authors: Yingchun Lu, Yang Liu, Jie Huang, Huaguo Liang, Zhengfeng Huang, Jinlin Chen, Xiumin Xu, Liang Yao
Institutions: Hefei University of Technology, Anhui University
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4415958233