Arbiter physical unclonable function (APUF) is a hardware security primitive that generates security keys by utilizing unavoidable process variations during chip manufacturing. However, the structure based on linear additive function makes it vulnerable to machine learning (ML) attacks. This paper proposes a parallel feedback obfuscation PUF (PFO PUF) design, which uses intermediate arbitration signals of the lower-layer APUF to generate the hidden challenge of the upper-layer APUF, enhancing the overall nonlinearity of the structure. The obfuscation module makes weight judgment for intermediate arbitration signals of upper-layer and lower-layer APUFs, which obfuscates the real response of PUF. We further design a variant of PFO PUF called reconfigured challenge obfuscation PFO PUF (RPFO PUF) and propose its lightweight device authentication protocol. RPFO PUF enhances the resistance of the original PFO PUF against reverse engineering (RE) and improves its Strict Avalanche Criterion (SAC) characteristic by reordering the challenges and incorporating weak PUF responses. The proposed PFO PUF and RPFO PUF were comprehensively evaluated via Python-based simulations and FPGA measurements. In Python simulations, both designs show strong resistance to state-of-the-art ML attacks, with logistic regression (LR), support vector machine (SVM), and covariance matrix adaptation evolution strategies (CMA-ES) yielding near 50% prediction accuracies under various PUF configurations. Although deep neural network (DNN) achieves up to 69.52% prediction accuracy on the PFO PUF, it drops to ∼50% on the RPFO PUF. FPGA results further confirm this, with the (32, 11)-RPFO PUF achieving a maximum prediction accuracy of only 51.47% across all four ML attacks. Moreover, both designs incur low hardware overheads, requiring just 743 and 2145 gate equivalents (GEs), respectively.

Strong physical unclonable functions (PUFs) are promising circuits for lightweight Internet of Things (IoT) authentication and security. However, existing strong PUFs exhibit very low cryptographic nonlinearity (NL), making them vulnerable to machine learning (ML) modeling and cryptanalytic attack. To address this issue, we propose the Bent function PUF (BF PUF) based on Maiorana-McFarland (M-M) constructed Bent functions, which obfuscates the responses of the strong PUF to enhance resistance against modeling attacks. The core idea is to employ the M-M construction method for Bent functions to ensure maximum cryptographic NL to resist modeling attacks. A Feistel network is configured using weak PUF responses as keys to achieve device-specific and unpredictable mappings of input challenges while meeting the requirements of the M-M Bent function construction. A Python-based model of the BF PUF was developed, and simulation results indicate that the cryptographic NL of the proposed BF PUF outperformsk-xorarbiter PUFs (APUFs) (${k} =2$, 4, 6). The proposed BF PUF was also implemented and evaluated on the FPGA hardware platform. The experimental results show that under modeling attacks using four ML algorithms—logistic regression (LR), artificial neural networks (ANNs), deep neural networks (DNNs), and covariance matrix adaptation evolution strategies (CMA-ES)—the best prediction accuracy under these four modeling attack algorithms is 52.60%. The reliability under temperature fluctuations ranging from$- 10~^{\circ }$C to$80~^{\circ }$C is between 84.20% and 99.78%.

Physical Unclonable Functions (PUF) are important hardware security primitives used for generating keys and identity authentication, with wide applications in the Internet of Things security. However, the security of strong PUF faces serious threats from modeling attacks, especially in the case of Arbiter PUF and their variants that involve additive linear integration of entropy sources. This paper proposes a Ring-Oscillator-Integrated-Linear-Feedback-Shift-Register-based PUF (ROinLFSR PUF) that achieves immunity to modeling attacks by highly nonlinearly integrating independent responses from weak RO PUFs using a configurable LFSR. To increase the efficiency of entropy extraction in hardware resources, dual entropy sources extraction is performed on the period and duty cycle of RO. Python simulation and FPGA experimental results demonstrate that the proposed PUF has intrinsic resilience against modeling attacks. And the proposed PUF achieves good results in reliability, uniqueness, uniformity, and randomness.

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.

The physical unclonable function (PUF) is a hardware security primitive that is used to generate secret keys or identity authentication for chips using random manufacturing process variation (MPV). The PUF based on ring oscillator (RO PUF) has been extensively studied in recent years because of its high robustness and ease of design. Although the performance has been optimized in previous studies, several uniqueness, reliability, and theoretical foundation concerns still remain. This article presents a transistor-level parameters-based quantitative theoretical model, which clearly reveals several design guidelines for improving the reliability of RO PUF. Furthermore, a PUF based on a feedback ring oscillator (RO) structure is proposed, which combined the RO topology and the drafting effect of XOR gates to enhance the uniqueness and reliability. The correctness of the theoretical model was verified by the SPICE simulation experiment result. And in the FPGA experiment result, the uniqueness and the reliability of feedback RO PUF using the same hardware resources on the same chip was superior to that of RO PUF. The theoretical research method of RO PUF used can be widely applied to other PUFs using ring topology and feedback RO PUF is a great substitute for RO PUF.