Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-08593-7_7 | TRUST |
Field | DocType | Citations |
Bistability,Computer science,Computer security,Field-programmable gate array,Electronic engineering,Theoretical computer science,Gate array,Physical unclonable function,Artificial neural network | Conference | 7 |
PageRank | References | Authors |
0.49 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dieter Schuster | 1 | 7 | 0.49 |
| Robert Hesselbarth | 2 | 11 | 1.65 |