OPUF: Obfuscated PUF against Machine Learning Modeling Attacks

Physical Unclonable Functions (PUFs) are vulnerable to machine learning modeling attacks that can predict their responses. To counter this threat, we introduce Obfuscated PUF (OPUF), a novel PUF design that employs advanced obfuscation techniques to enhance security. OPUF significantly outperforms existing PUFs in terms of resistance to machine learning attacks, including logistic regression (LR) and multilayer perceptron (MLP). It achieves a 50.69% and 49.54% accuracy reduction in LR and MLP attacks, respectively, compared to the best-performing existing PUFs. Moreover, OPUF maintains excellent quality metrics, with 50% uniqueness and 50.15% uniformity, demonstrating its inherent randomness and unpredictability. OPUF achieves its enhanced security through a multi-layered architecture that incorporates obfuscation techniques and nonlinear properties. By introducing internal challenges that are difficult for attackers to access, OPUF effectively thwarts modeling attempts. Our experimental results demonstrate OPUF’s superior performance in resisting various machine learning attacks, even when faced with extensive training data. OPUF offers a promising solution for secure hardware implementations, addressing the critical need for PUFs that can withstand the challenges posed by modern machine learning attacks.