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Support Vector Machines for Improved IP Detection with Soft Physical Hash Functions

  • Ludovic-Henri Gustin
  • François Durvaux
  • Stéphanie KerckhofEmail author
  • François-Xavier Standaert
  • Michel Verleysen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8622)

Abstract

Side-channel analysis is a powerful tool to extract secret information from microelectronic devices. Its most frequently considered application is destructive, i.e. key recovery attacks against cryptographic implementations. More recently, it has also been considered constructively, in the context of intellectual property protection/detection, e.g. through the use of side-channel based watermarks or soft physical hash functions. The latter solution is interesting from the application point-of-view, because it does not require any modification of the designs to protect (hence it implies no performance losses). Previous works in this direction have exploited simple (correlation-based) statistical tools in different (more or less challenging) scenarios. In this paper, we investigate the use of support vector machines for this purpose. We first argue that their single-class extension is naturally suited to the problem of intellectual property detection. We then show experimentally that they allow dealing with more complex scenarios than previously published, hence extending the relevance and applicability of soft physical hash functions.

Keywords

Support Vector Machine Intellectual Property Hash Function Similarity Score Block Cipher 
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.

Notes

Acknowledgements

This work has been funded in parts by the Walloon region WIST program project MIPSs and by the European Commission through the ERC project 280141 (acronym CRASH). François-Xavier Standaert is an Associate Researcher of the Belgian Fund for Scientific Research (FNRS-F.R.S.). Stéphanie Kerckhof is a PhD student funded by a FRIA grant, Belgium.

Supplementary material

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ludovic-Henri Gustin
    • 1
    • 2
  • François Durvaux
    • 1
  • Stéphanie Kerckhof
    • 1
    Email author
  • François-Xavier Standaert
    • 1
  • Michel Verleysen
    • 2
  1. 1.Crypto Group - ICTEAMUniversité catholique de LouvainLouvain-la-NeuveBelgium
  2. 2.Machine Learning Group - ICTEAMUniversité catholique de LouvainLouvain-la-NeuveBelgium

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