Cryptology ePrint Archive: Report 2017/1134

Machine Learning Attacks on PolyPUF, OB-PUF, RPUF, and PUF-FSM

Jeroen Delvaux

Abstract: A physically unclonable function (PUF) is a circuit of which the input–output behavior is designed to be sensitive to the random variations of its manufacturing process. This building block hence facilitates the authentication of any given device in a population of identically laid-out silicon chips, similar to the biometric authentication of a human. The focus and novelty of this work is the development of efficient impersonation attacks on the following four PUF-based authentication protocols: (1) the so-called PolyPUF protocol of Konigsmark, Chen, and Wong, as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2016, (2) the so-called OB-PUF protocol of Gao, Li, Ma, Al-Sarawi, Kavehei, Abbott, and Ranasinghe, as presented at the IEEE conference PerCom 2016, (3) the so-called RPUF protocol of Ye, Hu, and Li, as presented at the IEEE conference AsianHOST 2016, and (4) the so-called PUF–FSM protocol of Gao, Ma, Al-Sarawi, Abbott, and Ranasinghe, as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2017. The common flaw of all four designs is that the use of lightweight obfuscation logic provides insufficient protection against machine learning attacks.

Category / Keywords: physically unclonable functions, entity authentication, machine learning

Date: received 23 Nov 2017, last revised 19 Dec 2017

Contact author: jeroen delvaux at esat kuleuven be

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Version: 20171219:143126 (All versions of this report)

Short URL: ia.cr/2017/1134

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