Lightweight obfuscation techniques for modeling attacks resistant PUFs

Abstract

Building lightweight security for low-cost pervasive
devices is a major challenge considering the design requirements
of a small footprint and low power consumption. Physical Unclonable
Functions (PUFs) have emerged as a promising technology to
provide a low-cost authentication for such devices. By exploiting
intrinsic manufacturing process variations, PUFs are able to
generate unique and apparently random chip identifiers. Strong-
PUFs represent a variant of PUFs that have been suggested
for lightweight authentication applications. Unfortunately, many
of the Strong-PUFs have been shown to be susceptible to
modelling attacks (i.e., using machine learning techniques) in
which an adversary has access to challenge and response pairs.
In this study, we propose an obfuscation technique during postprocessing
of Strong-PUF responses to increase the resilience
against machine learning attacks. We conduct machine learning
experiments using Support Vector Machines and Artificial Neural
Networks on two Strong-PUFs: a 32-bit Arbiter-PUF and a 2-
XOR 32-bit Arbiter-PUF. The predictability of the 32-bit Arbiter-
PUF is reduced to ~ 70% by using an obfuscation technique.
Combining the obfuscation technique with 2-XOR 32-bit Arbiter-
PUF helps to reduce the predictability to ~ 64%. More reduction
in predictability has been observed in an XOR Arbiter-PUF
because this PUF architecture has a good uniformity. The area
overhead with an obfuscation technique consumes only 788 and
1080 gate equivalents for the 32-bit Arbiter-PUF and 2-XOR 32-
bit Arbiter-PUF, respectively.

More information

Identifiers

ORCID for Basel Halak: orcid.org/0000-0003-3470-7226

ORCID for Mark Zwolinski: orcid.org/0000-0002-2230-625X

Catalogue record

Export record

Altmetrics