Security Scalability of Arbiter PUF Designs

Physically Unclonable Functions (PUFs) are hardware security primitives that can offer an alternative lightweight security solution for authenticating constrained Internet of Things (IoT) devices. However, PUFs are susceptible to modeling attacks, requiring the adoption of various design approaches to increase their resiliency. Many research efforts propose design approaches that offer better security against modeling attacks. This work investigates state-of-the-art modeling attacks performed on well-known Arbiter-based PUF architectures highlighting the best-fit modeling algorithm for different design approaches. Furthermore, the area efficiency of studied PUF designs is examined, and the optimal PUF design approaches for various area constraints are suggested. Such an assessment is required to evaluate PUF security accurately and guide the PUF community toward better practices. The findings revealed that some machine-learning algorithms performed better on a particular design. Additionally, when considering area overhead, we found that some PUF designs offer less security per area unit than their simpler counterparts. Accordingly, certain design elements are more efficient and add more security.