Efficient Attacks on Strong PUFs via Covariance and Boolean Modeling

Abstract

The physical unclonable function (PUF) is a widely used hardware security primitive. Before hacking into a PUF-protected system, intruders typically initiate attacks on the PUF as the first step. Many strong PUF designs have been proposed to thwart non-invasive attacks that exploit acquired CRPs. In this work, we propose a general framework for efficient attacks on strong PUFs by investigating from two perspectives, namely, statistical covariances in the challenge space and the design dependency among PUF compositions. The framework consists of two novel attack methods against a wide range of PUF families, including XOR APUFs, interpose PUFs, and bistable ring (BR)-PUFs. It can also exploit the knowledge of reliability information to improve attack efficiency with gradient optimization. We evaluate our proposed attacks through extensive experiments, running both software-based simulation and hardware implementations on FPGAs to compare with corresponding SOTA works. Considerable effort has been made in ensuring identical software/hardware conditions for a fair comparison. The results demonstrate that our framework significantly outperforms SOTA results. Moreover, we show that our framework can efficiently attack diverse PUF families built from entirely different types, while almost all existing works solely focused on attacking one or very limited number of PUF designs.