Citation: Li, G.; Mursi, K.T. A
Subspace Pre-Learning Strategy to
Break the Interpose PUF. Electronics
2022, 11, 1049. https://doi.org/
10.3390/electronics11071049
Academic Editor: Cheng-Chi Lee
Received: 8 March 2022
Accepted: 26 March 2022
Published: 27 March 2022
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Article
A Subspace Pre-Learning Strategy to Break the Interpose PUF
Gaoxiang Li
1,
* and Khalid T. Mursi
2
1
Department of Computer Science, Texas Tech University, Lubbock, TX 79709, USA
2
Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah,
Jeddah 21959, Saudi Arabia; kmursi@uj.edu.sa
* Correspondence: gaoli@ttu.edu
Abstract:
Physical Unclonable Functions (PUFs) are promising security primitives for resource-
constrained IoT devices. A critical aspect of PUF security research is to identify all potential security
risks. This information about vulnerabilities is beneficial for both PUF developers and PUF-using
application developers in terms of designing new PUFs to mitigate existing risks and avoid vulnerable
PUFs. Recently, a PUF structure called Interpose PUF (IPUF) was proposed, which claims to be
resistant to reliability attacks and machine learning modeling attacks. Related studies on this secure
PUF design have demonstrated that some IPUFs can still be broken, but large IPUFs may remain
secure against all known modeling attacks. In addition, all these studies either focus on plain
challenge–response pair attacks or require prior knowledge of IPUF architecture implementation.
However, depending on the claim of attack resistance to reliability attacks, we can employ a different
attack approach to break IPUFs. In this paper, we describe a subspace pre-learning-based attack
method that can rapidly and accurately break the IPUFs that were treated as secure in the earlier
study, revealing a vulnerability in IPUFs if the open interface conforms to the way challenge–response
data are accessed by the subspace pre-learning-based attack method.
Keywords:
IoT security; physical unclonable function; interpose PUF; machine learning
modeling attack
1. Introduction
The Internet of Things (IoT) has wide and deep participation in business and everyday
life. With the exponential rise of IoT requirements, communication security has attracted
increased attention [
1
]. However, considering most traditional cryptographic techniques,
which require persistent memory to achieve the desired level of security, many IoT devices
are resource-constrained and cannot support traditional cryptographic protocols [
2
,
3
]. Phys-
ical Unclonable Functions (PUFs) were proposed as a potential replacement for classical
cryptography in IoT devices [
4
–
6
], leveraging small physical variations of a small number
of transistors to produce responses unique to the individual circuit. Because of their low
resource requirements, PUFs are excellent candidates for hardware primitives that can be
utilized to construct security protocols on network nodes with limited resources.
However, before adopting PUFs as a trusted security function, they must be examined
to identify all possible security vulnerabilities, such as vulnerabilities to machine learning
(ML) modeling attacks [
7
–
10
] and reliability-based attacks [
11
,
12
]. In machine learning
modeling attacks, the attacker eavesdrops on-air packets between IoTs in order to collect
enough plain challenge–response pairs (CRPs) to build a model. Then, the attacker inputs
the collected challenges, as features, and responses, as class labels, into an ML model
targeted at having a learned model for future response prediction. In reliability-based
attacks, the attacker applies pre-set challenges to the PUF with an open interface and
collects specific CRPs. Furthermore, CRPs obtained through freely queried can be easily
used to break PUFs by utilizing the reliability information of these CRPs.
Interpose PUF (IPUF) is proposed by Nguyen et al., [
13
] to mitigate the two above-
mentioned classes of attack. Studies in [
8
,
13
] show IPUFs could withstand various existing
Electronics 2022, 11, 1049. https://doi.org/10.3390/electronics11071049 https://www.mdpi.com/journal/electronics
