Semi-Supervised Template Attack

  • Liran Lerman
  • Stephane Fernandes Medeiros
  • Nikita Veshchikov
  • Cédric Meuter
  • Gianluca Bontempi
  • Olivier Markowitch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7864)


Side channel attacks take advantage of information leakages in cryptographic devices. Template attacks form a family of side channel attacks which is reputed to be extremely effective. This kind of attacks assumes that the attacker fully controls a cryptographic device before attacking a similar one. In this paper, we propose to relax this assumption by generalizing the template attack using a method based on a semi-supervised learning strategy. The effectiveness of our proposal is confirmed by software simulations, by experiments on a 8-bit microcontroller and by a comparison to a template attack as well as to two supervised machine learning methods.


Side channel attack Template attack Power analysis Semi-supervised learning Clustering Hamming weight 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Liran Lerman
    • 1
    • 2
  • Stephane Fernandes Medeiros
    • 1
  • Nikita Veshchikov
    • 1
  • Cédric Meuter
    • 3
  • Gianluca Bontempi
    • 2
  • Olivier Markowitch
    • 1
  1. 1.Quality and security of Information Systems, Département d’informatiqueUniversité Libre de BruxellesBelgium
  2. 2.Machine Learning Group, Département d’informatiqueUniversité Libre de BruxellesBelgium
  3. 3.Atos WorldlineBelgium

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