Welcome to the resource topic for 2024/446
Title:
Estimating the Unpredictability of Multi-Bit Strong PUF Classes
Authors: Ahmed Bendary, Wendson A. S. Barbosa, Andrew Pomerance, C. Emre Koksal
Abstract: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.
ePrint: https://eprint.iacr.org/2024/446
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