Policy Gradients for Cryptanalysis

  • Frank Sehnke
  • Christian Osendorfer
  • Jan Sölter
  • Jürgen Schmidhuber
  • Ulrich Rührmair
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6354)


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.


Propagation Delay Prediction Rate Evolution Strategy Physical Unclonable Function Policy Gradient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Frank Sehnke
    • 1
  • Christian Osendorfer
    • 1
  • Jan Sölter
    • 2
  • Jürgen Schmidhuber
    • 3
    • 4
  • Ulrich Rührmair
    • 1
  1. 1.Faculty of Computer ScienceTechnische Universität MünchenGermany
  2. 2.Faculty of BiologyFreie Universität BerlinGermany
  3. 3.Istituto Dalle Molle di Studi sull’Intelligenza ArtificialeLuganoSwitzerland
  4. 4.Faculty of Computer ScienceUniversitá della Svizzera italianaLuganoSwitzerland

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