Abstract
This paper presents an analysis of a bistable ring physical unclonable function (BR-PUF) implemented on a field-programmable gate array (FPGA) using a single layer artificial neural network (ANN). The BR-PUF was proposed as a promising circuit-based strong PUF candidate, given that a simple model for its behaviour is unknown by now and hence modeling-based attacks would be hard. In contrast to this, we were able to find a strongly linear influence in the mapping of challenges to responses in this architecture. Further, we show how an alternative implementation of a bistable ring, the twisted bistable ring PUF (TBR-PUF), leads to an improved response behaviour. The effectiveness and a possible explaination of the improvements is demonstrated using our machine learning analysis approach.
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Schuster, D., Hesselbarth, R. (2014). Evaluation of Bistable Ring PUFs Using Single Layer Neural Networks. In: Holz, T., Ioannidis, S. (eds) Trust and Trustworthy Computing. Trust 2014. Lecture Notes in Computer Science, vol 8564. Springer, Cham. https://doi.org/10.1007/978-3-319-08593-7_7
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DOI: https://doi.org/10.1007/978-3-319-08593-7_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08592-0
Online ISBN: 978-3-319-08593-7
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