A Time Series Approach for Profiling Attack

  • Liran Lerman
  • Gianluca Bontempi
  • Souhaib Ben Taieb
  • Olivier Markowitch
Conference paper

DOI: 10.1007/978-3-642-41224-0_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8204)
Cite this paper as:
Lerman L., Bontempi G., Ben Taieb S., Markowitch O. (2013) A Time Series Approach for Profiling Attack. In: Gierlichs B., Guilley S., Mukhopadhyay D. (eds) Security, Privacy, and Applied Cryptography Engineering. SPACE 2013. Lecture Notes in Computer Science, vol 8204. Springer, Berlin, Heidelberg

Abstract

The goal of a profiling attack is to challenge the security of a cryptographic device in the worst case scenario. Though template attack is reputed as the strongest power analysis attack, they effectiveness is strongly dependent on the validity of the Gaussian assumption. This led recently to the appearance of nonparametric approaches, often based on machine learning strategies. Though these approaches outperform template attack, they tend to neglect the potential source of information available in the temporal dependencies between power values. In this paper, we propose an original multi-class profiling attack that takes into account the temporal dependence of power traces. The experimental study shows that the time series analysis approach is competitive and often better than static classification alternatives.

Keywords

side-channel attack power analysis machine learning time series classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Liran Lerman
    • 1
    • 2
  • Gianluca Bontempi
    • 2
  • Souhaib Ben Taieb
    • 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

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