Efficient Template Attacks Based on Probabilistic Multi-class Support Vector Machines

  • Timo Bartkewitz
  • Kerstin Lemke-Rust
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7771)


Common template attacks are probabilistic relying on the multivariate Gaussian distribution regarding the noise of the device under attack. Though this is a realistic assumption, numerical problems are likely to occur in practice due to evaluation in higher dimensions. To avoid this, a feature selection is applied to identify points in time that contribute most information to an attack. An alternative to common template attacks is to apply machine learning in form of support vector machines (SVMs). Recent works brought out approaches that produce comparable results, respectively better in the presence of noise, but still not optimal in terms of efficiency and performance. In this work we show how to adapt the SVM template approach in order to considerably reduce the effort while carrying out the attack and how to better exploit the side-channel information under the assumption of an attack model with a strict order, e.g. Hamming weight model.


Power Analysis Template Attacks Machine Learning Support Vector Machines 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Timo Bartkewitz
    • 1
  • Kerstin Lemke-Rust
    • 1
  1. 1.Department of Computer ScienceBonn-Rhine-Sieg University of Applied SciencesSankt AugustinGermany

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