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Cost-efficient design for modeling attacks resistant PUFs

Cost-efficient design for modeling attacks resistant PUFs
Cost-efficient design for modeling attacks resistant PUFs

Physical Unclonable Functions (PUFs) exploit the intrinsic manufacturing process variations to generate a unique signature for each silicon chip; this technology allows building lightweight cryptographic primitive suitable for resource-constrained devices. However, the vast majority of existing PUF design is susceptible to modeling attacks using machine learning technique, this means it is possible for an adversary to build a mathematical clone of the PUF that have the same challenge/response behavior of the device. Existing approaches to solve this problem include the use of hash functions, which can be prohibitively expensive and render PUF technology as the suitable candidate for lightweight security. This work presents a challenge permutation and substitution techniques which are both area and energy efficient. We implemented two examples of the proposed solution in 65-nm CMOS technology, the first using a delay-based structure design (an Arbiter-PUF), and the second using sub-Threshold current design (two-choose-one PUF or TCO-PUF). The resiliency of both architectures against modeling attacks is tested using an artificial neural network machine learning algorithm. The experiment results show that it is possible to reduce the predictability of PUFs to less than 70% and a fractional area and power costs compared to existing hash function approaches.

Arbiter-puf, Machine learning, Physical unclonable function (puf), Security, Tco-puf
467-472
Institute of Electrical and Electronics Engineers Inc.
Mispan, Mohd Syafiq
568c91c3-c200-441c-887b-8f299635b94e
Su, Haibo
07117108-5e87-4450-9853-1d4c12d387ca
Zwolinski, Mark
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Mispan, Mohd Syafiq
568c91c3-c200-441c-887b-8f299635b94e
Su, Haibo
07117108-5e87-4450-9853-1d4c12d387ca
Zwolinski, Mark
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33

Mispan, Mohd Syafiq, Su, Haibo, Zwolinski, Mark and Halak, Basel (2018) Cost-efficient design for modeling attacks resistant PUFs. In Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc. pp. 467-472 . (doi:10.23919/DATE.2018.8342054).

Record type: Conference or Workshop Item (Paper)

Abstract

Physical Unclonable Functions (PUFs) exploit the intrinsic manufacturing process variations to generate a unique signature for each silicon chip; this technology allows building lightweight cryptographic primitive suitable for resource-constrained devices. However, the vast majority of existing PUF design is susceptible to modeling attacks using machine learning technique, this means it is possible for an adversary to build a mathematical clone of the PUF that have the same challenge/response behavior of the device. Existing approaches to solve this problem include the use of hash functions, which can be prohibitively expensive and render PUF technology as the suitable candidate for lightweight security. This work presents a challenge permutation and substitution techniques which are both area and energy efficient. We implemented two examples of the proposed solution in 65-nm CMOS technology, the first using a delay-based structure design (an Arbiter-PUF), and the second using sub-Threshold current design (two-choose-one PUF or TCO-PUF). The resiliency of both architectures against modeling attacks is tested using an artificial neural network machine learning algorithm. The experiment results show that it is possible to reduce the predictability of PUFs to less than 70% and a fractional area and power costs compared to existing hash function approaches.

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More information

e-pub ahead of print date: 19 March 2018
Published date: 23 April 2018
Venue - Dates: 2018 Design, Automation and Test in Europe Conference and Exhibition, Dresden, Germany, 2018-03-19 - 2018-03-23
Keywords: Arbiter-puf, Machine learning, Physical unclonable function (puf), Security, Tco-puf

Identifiers

Local EPrints ID: 423244
URI: https://eprints.soton.ac.uk/id/eprint/423244
PURE UUID: 01feb047-b53d-4289-8603-d221037bc401
ORCID for Mark Zwolinski: ORCID iD orcid.org/0000-0002-2230-625X
ORCID for Basel Halak: ORCID iD orcid.org/0000-0003-3470-7226

Catalogue record

Date deposited: 19 Sep 2018 16:30
Last modified: 15 Oct 2019 00:56

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Contributors

Author: Mohd Syafiq Mispan
Author: Haibo Su
Author: Mark Zwolinski ORCID iD
Author: Basel Halak ORCID iD

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