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Having no mathematical model may not secure PUFs

  • CHES 2016
  • Published:
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Abstract

Although numerous attacks revealed the vulnerability of different PUF families to noninvasive machine learning (ML) attacks, the question is still open whether all PUFs might be learnable. Until now, virtually all ML attacks rely on the assumption that a mathematical model of the PUF functionality is known a priori. However, this is not always the case, and attention should be paid to this important aspect of ML attacks. This paper aims to address this issue by providing a provable framework for ML attacks against a PUF family, whose underlying mathematical model is unknown. We prove that this PUF family is inherently vulnerable to our novel probably approximately correct learning framework. We apply our ML algorithm on the Bistable Ring PUF (BR-PUF) family, which is one of the most interesting and prime examples of a PUF with an unknown mathematical model. We practically evaluate our ML algorithm through extensive experiments on BR-PUFs implemented on field-programmable gate arrays. In line with our theoretical findings, our experimental results strongly confirm the effectiveness and applicability of our attack. This is also interesting since our complex proof heavily relies on the spectral properties of Boolean functions, which are known to hold only asymptotically. Along with this proof, we further provide the theorem that all PUFs must have some challenge bit positions, which have larger influences on the responses than other challenge bits.

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Acknowledgements

We would like to thank Prof. Dr. Frederik Armknecht for the fruitful discussion as well as pointing out the Siegenthaler’s paper. We acknowledge the effort made by Julian Fietkau, who has prepared the experimental setup required to do the K-junta test. Furthermore, the authors greatly appreciate the support that they received from Helmholtz Research School on Security Technologies.

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Authors and Affiliations

  1. Security in Telecommunications, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany

    Fatemeh Ganji, Shahin Tajik, Fabian Fäßler & Jean-Pierre Seifert

Authors
  1. Fatemeh Ganji
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  2. Shahin Tajik
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  3. Fabian Fäßler
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  4. Jean-Pierre Seifert
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Corresponding author

Correspondence to Fatemeh Ganji.

Additional information

This paper is an extended version of the paper [17], presented at CHES 2016. In comparison with it, the novelties are: Further experimental results on property testing and machine learning have been reported. Moreover, these results and their practical implications have been subsequently discussed. Last but not least, broader theoretical perspectives which offer new insights necessary for understanding our framework have been adopted and discussed.

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Ganji, F., Tajik, S., Fäßler, F. et al. Having no mathematical model may not secure PUFs. J Cryptogr Eng 7, 113–128 (2017). https://doi.org/10.1007/s13389-017-0159-4

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