Authentication scheme resistant to machine learning based on obfuscation of multiple PUF responses

Tianming Ni, Hao Wu, Fei Li, Mu Nie, Yun Liu, Ang Hu, Jingchang Bian,
Authentication scheme resistant to machine learning based on obfuscation of multiple PUF responses,
Microelectronics Journal,
Volume 166,
2025,
106867,
ISSN 1879-2391,
https://doi.org/10.1016/j.mejo.2025.106867.
(https://www.sciencedirect.com/science/article/pii/S1879239125003169)
Abstract: Traditional authentication protocols leveraging Physical Unclonable Functions (PUFs) face vulnerabilities to modeling attacks, this paper proposes multi-Arbiter-PUF obfuscation PUF (MAO PUF) architecture. This architecture incorporates n Arbiter PUFs (APUFs) and a k-stage Linear Feedback Shift Register (LFSR), where the responses from n APUFs are employed to obfuscate the LFSR's configuration parameters, thereby enhancing resistance against machine learning-based modeling attacks. The (n,k)-MAO PUF architecture was implemented on a Xilinx Virtex-7 Field Programmable Gate Array (FPGA) platform, demonstrating that the (5,3)-MAO PUF achieves an optimal balance between resource overhead and performance metrics. The (5,3)-MAO PUF depends on responses from five APUFs to obfuscate both the initial seed and feedback coefficients of a 3-stage LFSR. This approach reduces the prediction accuracy of three mainstream machine learning attacks to nearly 50 %, while maintaining statistical characteristics close to ideal values. Furthermore, based on the structural characteristics of (n,k)-MAO PUF, we further propose a novel highly secure authentication protocol which is particularly suitable for the Internet of Things (IoT) systems.
Keywords: Physical unclonable function (PUF); Machine learning attack; FPGAs; Hardware security; Authentication protocol