PFO PUF: A Lightweight Parallel Feed Obfuscation PUF Resistant to Machine Learning Attacks
Zhengfeng Huang, Yankun Lin, Fansheng Zeng, Jingchang Bian, Zhao Yang, Huaguo Liang, Yingchun Lu, Liang Yao, Xiaoqing Wen, Tianming Ni · 2024
Arbiter Physically Unclonable Functions (APUFs) are hardware security primitives that leverage manufacturing process variation to generate security keys. They can produce exponential challenge-response pairs (CRPs) with minimal hardware overhead. However, the symmetric nature of linear additive functions makes them vulnerable to modeling attacks rooted in machine learning. To address this issue, this paper introduces a novel design called Parallel Feed Obfuscation PUF (PFO PUF). In this approach, the intermediate decision signals from the lower APUF are used as a concealed challenge for the upper APUF, enhancing the overall nonlinearity of the dual-APUF. Additionally, obfuscation modules are employed to determine the weights of the intermediate decision signals from both the upper and lower APUFs, protecting the actual response. Experimental results demonstrate that the proposed PFO PUF effectively withstands four advanced machine learning attack algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Deep Feedforward Neural Network (DFNN), and Efficient CANDECOMP/PARAFAC Tensor Regression Network (ECPTRN). The prediction accuracy of these four algorithms is consistently below 66.30%. Compared with other enhanced structures based on APUF, PFO-PUF only uses 493 LUTs and has lower resource overhead.