Cryptology ePrint Archive: Report 2019/799

Breaking the Lightweight Secure PUF: Understanding the Relation of Input Transformations and Machine Learning Resistance

Nils Wisiol and Georg T. Becker and Marian Margraf and Tudor A. A. Soroceanu and Johannes Tobisch and Benjamin Zengin

Abstract: Physical Unclonable Functions (PUFs) and, in particular, XOR Arbiter PUFs have gained much research interest as an authentication mechanism for embedded systems. One of the biggest problems of (strong) PUFs is their vulnerability to so called machine learning attacks. In this paper we take a closer look at one aspect of machine learning attacks that has not yet gained the needed attention: the generation of the sub-challenges in XOR Arbiter PUFs fed to the individual Arbiter PUFs. Specifically, we look at one of the most popular ways to generate sub-challenges based on a combination of permutations and XORs as it has been described for the "Lightweight Secure PUF". Previous research suggested that using such a sub-challenge generation increases the machine learning resistance significantly.

Our contribution in the field of sub-challenge generation is three-fold: First, drastically improving attack results by Rührmair et al., we describe a novel attack that can break the Lightweight Secure PUF in time roughly equivalent to an XOR Arbiter PUF without transformation of the challenge input. Second, we give a mathematical model that gives insight into the weakness of the Lightweight Secure PUF and provides a way to study generation of sub-challenges in general. Third, we propose a new, efficient, and cost-effective way for sub-challenge generation that mitigates the attack strategy we used and outperforms the Lightweight Secure PUF in both machine learning resistance and resource overhead.

Category / Keywords: applications / Physically Unclonable Function, Machine Learning, Modelling Attack

Date: received 10 Jul 2019

Contact author: mail at nils-wisiol de

Available format(s): PDF | BibTeX Citation

Version: 20190714:154809 (All versions of this report)

Short URL: ia.cr/2019/799


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