Policy gradients for cryptanalysis

Frank Sehnke, Christian Osendorfer, Jan Sölter, Jürgen Schmidhuber, Ulrich Rührmair

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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. © 2010 Springer-Verlag Berlin Heidelberg.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages168-177
Number of pages10
DOIs
StatePublished - Nov 8 2010
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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