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On the Performance of Convolutional Neural Networks for Side-Channel Analysis

  • Stjepan Picek
  • Ioannis Petros Samiotis
  • Jaehun Kim
  • Annelie Heuser
  • Shivam Bhasin
  • Axel Legay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11348)

Abstract

In this work, we ask a question whether Convolutional Neural Networks are more suitable for side-channel attacks than some other machine learning techniques and if yes, in what situations. Our results point that Convolutional Neural Networks indeed outperform machine learning in several scenarios when considering accuracy. Still, often there is no compelling reason to use such a complex technique. In fact, if comparing techniques without extra steps like preprocessing, we see an obvious advantage for Convolutional Neural Networks when the level of noise is small, and the number of measurements and features is high. The other tested settings show that simpler machine learning techniques, for a significantly lower computational cost, perform similarly or sometimes even better. The experiments with guessing entropy indicate that methods like Random Forest or XGBoost could perform better than Convolutional Neural Networks for the datasets we investigated.

Keywords

Side-channel analysis Machine learning Deep learning Convolutional Neural Networks 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Stjepan Picek
    • 1
  • Ioannis Petros Samiotis
    • 1
  • Jaehun Kim
    • 1
  • Annelie Heuser
    • 2
  • Shivam Bhasin
    • 3
  • Axel Legay
    • 4
  1. 1.Delft University of TechnologyDelftThe Netherlands
  2. 2.CNRS, IRISARennesFrance
  3. 3.Physical Analysis and Cryptographic Engineering, Temasek LaboratoriesNanyang Technological UniversitySingaporeSingapore
  4. 4.Inria, IRISARennesFrance

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