Name
Abstract | ||
|---|---|---|
So-called Physical Unclonable Functions are an emerging, new cryptographic and security primitive. They can potentially replace
secret binary keys in vulnerable hardware systems and have other security advantages. In this paper, we deal with the cryptanalysis
of this new primitive by use of machine learning methods. In particular, we investigate to what extent the security of circuit-based
PUFs can be challenged by a new machine learning technique named Policy Gradients with Parameter-based Exploration. Our findings
show that this technique has several important advantages in cryptanalysis of Physical Unclonable Functions compared to other
machine learning fields and to other policy gradient methods.
|
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15825-4_22 | Int. Conference on Artificial Neural Networks |
Keywords | DocType | Volume |
security advantage,physical unclonable,new machine,important advantage,policy gradient method,circuit-based pufs,policy gradients,new cryptographic,so-called physical unclonable functions,parameter-based exploration,machine learning,gradient method | Conference | 6354 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-15824-2 | 3 |
PageRank | References | Authors |
0.51 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Sehnke | 1 | 527 | 39.18 |
| Christian Osendorfer | 2 | 125 | 13.24 |
| Jan Sölter | 3 | 426 | 18.21 |
| Jürgen Schmidhuber | 4 | 17836 | 1238.63 |
| Ulrich Rührmair | 5 | 685 | 38.92 |