Evaluation of bistable ring PUFs using single layer neural networks

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. © 2014 Springer International Publishing.