Use of Thermistor Temperature Sensors for Cyber-Physical System Security.

Internet of Things (IoT) Physically Unclonable Function (PUF) cyber-physical systems security sensor PUF thermistor

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
10 Sep 2019
Historique:
received: 03 07 2019
revised: 22 08 2019
accepted: 03 09 2019
entrez: 13 9 2019
pubmed: 13 9 2019
medline: 13 9 2019
Statut: epublish

Résumé

The last few decades have seen a large proliferation in the prevalence of cyber-physical systems. This has been especially highlighted by the explosive growth in the number of Internet of Things (IoT) devices. Unfortunately, the increasing prevalence of these devices has begun to draw the attention of malicious entities which exploit them for their own gain. What makes these devices especially attractive is the various resource constraints present in these devices that make it difficult to add standard security features. Therefore, one intriguing research direction is creating security solutions out of already present components such as sensors. Physically Unclonable Functions (PUFs) are one potential solution that use intrinsic variations of the device manufacturing process for provisioning security. In this work, we propose a novel weak PUF design using thermistor temperature sensors. Our design uses the differences in resistance variation between thermistors in response to temperature change. To generate a PUF that is reliable across a range of temperatures, we use a response-generation algorithm that helps mitigate the effects of temperature variation on the thermistors. We tested the performance of our proposed design across a range of environmental operating conditions. From this we were able to evaluate the reliability of the proposed PUF with respect to variations in temperature and humidity. We also evaluated the PUF's uniqueness using Monte Carlo simulations.

Identifiants

pubmed: 31510093
pii: s19183905
doi: 10.3390/s19183905
pmc: PMC6767224
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Science Foundation
ID : 1738662

Références

Sensors (Basel). 2018 Dec 15;18(12):null
pubmed: 30558323
J Diabetes Sci Technol. 2017 Mar;11(2):207-212
pubmed: 27920270
Sensors (Basel). 2019 Jul 03;19(13):null
pubmed: 31277324
Sensors (Basel). 2018 Jul 26;18(8):null
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Sensors (Basel). 2019 May 28;19(11):null
pubmed: 31141896

Auteurs

Carson Labrado (C)

Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA.

Himanshu Thapliyal (H)

Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA. hthapliyal@uky.edu.

Stacy Prowell (S)

Cyber and Applied Data Analytics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

Teja Kuruganti (T)

Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

Classifications MeSH