Policy gradients for cryptanalysis

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

doi:10.1007/978-3-642-15825-4_22

Policy gradients for cryptanalysis

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	168-177
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-15825-4_22
State	Published - Nov 8 2010
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-15825-4_22

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title = "Policy gradients for cryptanalysis",

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. {\textcopyright} 2010 Springer-Verlag Berlin Heidelberg.",

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AU - Sehnke, Frank

AU - Osendorfer, Christian

AU - Sölter, Jan

AU - Schmidhuber, Jürgen

AU - Rührmair, Ulrich

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14

PY - 2010/11/8

Y1 - 2010/11/8

N2 - 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.

AB - 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.

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DO - 10.1007/978-3-642-15825-4_22

M3 - Conference contribution

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EP - 177

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Policy gradients for cryptanalysis

Abstract

Bibliographical note

ASJC Scopus subject areas

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