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Design of Robust, High-Entropy Strong PUFs via Weightless Neural Network

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Abstract

Physically unclonable functions (PUFs) have been explored as lightweight hardware primitives for the purpose of realizing robust security via strong authentication or secure key/ID generation. PUF harness manufacturing process variations for the purpose of generating binary keys or binary functions. An ideal strong PUF is a binary function that maps an m-bit input challenge to an uniquen-bit output response, making it attractive for authentication applications. Unfortunately, real strong PUF implementations suffer from reliability issues where the same challenge may produce different responses in the presence of noise. To overcome this problem, strong PUF leverages the availability of exponential number of challenge-response pairs (CRPs). A successful authentication event requires acquiring multiple CRPs and applying a threshold. In contrast, weak PUFs produce limited keys and are required to be highly reliable. Multiple techniques have been developed to achieve the necessary reliability. An additional prerequisite for strong PUFs is resilience against model-building attacks (cloning) by an adversary, who has observed a few CRPs, to prevent successful prediction of future CRPs. In this work, we first illustrate a strong PUF design that re-purposes a weightless neural network (WNN). Second, we showcase the robustness of WNN-based strong PUFs with respect to machine learning attacks, while providing desirable uniqueness and reliability metrics. Finally, we employ an initial entropy source of highly reliable weak PUF bits mapped to weightless neural networks (WNNs) for the purpose of creating a near-ideal strong PUF in terms of reliability. Our results show that it is possible to create highly reliable WNN–based strong PUFs with < 65 % ML accuracy by using as few as 32 initial reliable weak PUF bits.

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Funding

The authors received financial support from CAPES, CNPq, and FAPERJ for this work. This work is supported in part by grants from H2020-EUBR (grant no. 2568), NSF (grant no. 1421352), and Intel and CAPES Brazil (grant no. 88881.068163/2014-01)

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Authors and Affiliations

  1. Programa de Engenharia de Sistemas e Computação - COPPE, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil

    Leandro Santiago de Araújo & Felipe M. G. França

  2. Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA, USA

    Vinay C. Patil & Sandip Kundu

  3. Quality and Technology (Inmetro), National Institute of Metrology, Rio de Janeiro, Brazil

    Charles B. Prado

  4. Instituto de Matemática e Estatística, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil

    Tiago A. O. Alves & Leandro A. J. Marzulo

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  1. Leandro Santiago de Araújo
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  2. Vinay C. Patil
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  3. Charles B. Prado
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  4. Tiago A. O. Alves
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  5. Leandro A. J. Marzulo
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  6. Felipe M. G. França
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  7. Sandip Kundu
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Correspondence to Leandro Santiago de Araújo.

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Santiago de Araújo, L., C. Patil, V., B. Prado, C. et al. Design of Robust, High-Entropy Strong PUFs via Weightless Neural Network. J Hardw Syst Secur 3, 235–249 (2019). https://doi.org/10.1007/s41635-019-00071-z

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  • DOI: https://doi.org/10.1007/s41635-019-00071-z

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