Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks

  • Stjepan Picek
  • Annelie Heuser
  • Alan Jovic
  • Axel Legay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10239)

Abstract

Machine learning techniques represent a powerful paradigm in side-channel analysis, but they come with a price. Selecting the appropriate algorithm as well as the parameters can sometimes be a difficult task. Nevertheless, the results obtained usually justify such an effort. However, a large part of those results use simplification of the data relation and in fact do not consider allthe available information. In this paper, we analyze the hierarchical relation between the data and propose a novel hierarchical classification approach for side-channel analysis. With this technique, we are able to introduce two new attacks for machine learning side-channel analysis: Hierarchical attack and Structured attack. Our results show that both attacks can outperform machine learning techniques using the traditional approach as well as the template attack regarding accuracy. To support our claims, we give extensive experimental results and discuss the necessary conditions to conduct such attacks.

Keywords

Side-channel attacks Profiled scenario Machine learning techniques Hierarchical classification Hierarchical attack Structured attack 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Stjepan Picek
    • 1
  • Annelie Heuser
    • 2
  • Alan Jovic
    • 3
  • Axel Legay
    • 4
  1. 1.KU Leuven ESAT/COSIC and imecLeuven-HeverleeBelgium
  2. 2.IRISA/CNRSRennesFrance
  3. 3.University of Zagreb, Faculty of Electrical Engineering and ComputingZagrebCroatia
  4. 4.IRISA/InriaRennesFrance

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