Evaluating the Unpredictability of Multi-Bit Strong PUF Classes

Can Emre Koksal

Abstract:

With advances in machine learning (ML), cybersecurity solutions and security
primitives are becoming increasingly vulnerable to successful attacks.
Strong Physical Unclonable Functions (PUFs) emerge as a potential countermeasure
that offers high resistance to such attacks. In this paper, we introduce
a generalized attack model that leverages the collective responses of
multiple PUF chips within the same class to improve the prediction accuracy
of responses for unobserved challenges, in contrast to traditional single-chip
approaches. Furthermore, we propose an information-theoretic framework
for assessing the unpredictability of multi-bit strong PUF classes, demonstrating
that the Entropy Rate is a pivotal metric for evaluating their resilience
against ML attacks. Our proposed entropy rate estimation serves as
a model-agnostic, information-theoretic lower bound on the unpredictability
that holds regardless of the attack strategy used, including ML-based ones.
We argue that the Uniqueness measure, defined in terms of entropy, provides
a more precise and consistent evaluation compared to traditional metrics
based on Hamming distance. Additionally, we present a computationally
efficient method for calculating the finite-order Entropy Rate of the hybrid
Boolean network (HBN) PUF, addressing the challenges posed by high dimensionality.
The experimental results validate the high unpredictability
and resistance of the HBN PUF class against ML attacks.