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PUFs Physical Learning: Accelerating the Enrollment via Delay-Based Model Extraction

Publisher: IEEE

Abstract:The introduction of Physical Unclonable Functions (PUFs) has been originally motivated by their ability to resist physical attacks, particularly in anti-counterfeiting sc...View more
Abstract:
The introduction of Physical Unclonable Functions (PUFs) has been originally motivated by their ability to resist physical attacks, particularly in anti-counterfeiting scenarios. In these one-way functions, machine learning, cryptanalysis, and side-channel attacks are common attack vectors threatening the promised PUF's property of unclonability. These attacks often emulate a PUF by employing a large number of Challenge-Response Pairs (CRPs). Some solutions to defeat such attacks are based on a protocol, where a model of the underlying PUF primitives should be extracted during the enrollment phase. In this article, we introduce a novel physical cloning approach applicable to FPGA-based implementations, which allows extracting the PUF's unique physical characteristics with a few number of Challenge-Response Pairs (CRPs), that increases only linearly for a higher number of PUF components. Indeed, our proposed approach significantly accelerates the enrollment phase and makes complex enrollment protocols feasible. Our core idea relies on an on-chip delay sensor, which can be realized by ordinary FPGA components, measuring the unique characteristic of the PUF elements. We demonstrate the feasibility of our introduced technique by practical experiments on different FPGA platforms, cloning a couple of (complex) PUF constructions, i.e., XOR APUF, iPUF, composed of delay-based Arbiter PUFs.
Published in: IEEE Transactions on Emerging Topics in Computing ( Volume: 10, Issue: 3, 01 July-Sept. 2022)
Page(s): 1621 - 1632
Date of Publication: 30 September 2021
ISSN Information:
Publisher: IEEE
Funding Agency:

I. Introduction

Security in hardware implementations has increasingly pervaded throughout the connected world with different IoT applications and well-funded adversaries, where the separation between hacker groups and nation-state organizations has already vanished. Physical Unclonable Functions (PUFs), as one of the lightweight security primitives, are being used in IoT systems to defend against various attacks and boost the security in the form of multiple applications like key generation processes or authentication protocols [1]. PUFs exploit the inherent unpredictable physical characteristics of, e.g., silicon [2]. More precisely, these random characteristics come from the subtle mismatches during the Integrated Circuit (IC) manufacturing processes and are unique to every device. They are often called the fingerprint of the device. Among various types of PUFs, silicon PUFs [2] are applied more extensively in research and applications as they can query Challenge-Response Pairs (CRPs) within the chip without requiring any analog signal [3]. Ideally a PUF generates a reliable, unique, and unpredictable response r{0,1}m to a given random challenge c{0,1}n in ideal circumstances (e.g., under a constant temperature).

References

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