International Association
for Cryptologic Research

With the ongoing advances in machine learning (ML), cybersecurity solutions and security primitives are becoming increasingly vulnerable to successful attacks. Strong physically unclonable functions (PUFs) are a potential solution for providing high resistance to such attacks. In this paper, we propose a generalized attack model that leverages multiple chips jointly to minimize the cloning error. Our analysis shows that the entropy rate over different chips is a relevant measure to the new attack model as well as the multi-bit strong PUF classes. We explain the sources of randomness that affect unpredictability and its possible measures using models of state-of-the-art strong PUFs. Moreover, we utilize min-max entropy estimators to measure the unpredictability of multi-bit strong PUF classes for the first time in the PUF community. Finally, we provide experimental results for a multi-bit strong PUF class, the hybrid Boolean network PUF, showing its high unpredictability and resistance to ML attacks.