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International Symposium on Cyber Security, Cryptology, and Machine Learning

CSCML 2022: Cyber Security, Cryptology, and Machine Learning pp 29–46Cite as

Time, Memory and Accuracy Tradeoffs in Side-Channel Trace Profiling

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13301)

Abstract

Template attacks are one of the most powerful classes of side-channel attacks. Template attacks begin with an offline step, in which the side-channel traces are profiled, and decoders are created for each side-channel leak. In this paper, we analyze the compression step of the trace profiling process. This compression step, which is a central part of the decoder’s training process, is used to reduce the amount of time, memory consumption, and data required to successfully perform the attack; various practical methods have been proposed for this step, including one which uses an efficient means both for selecting the points of interest (POI) in the power trace and for preprocessing noisy data.

We investigate ways to improve the efficiency of the attack by implementing several compression methods which select the most informative power consumption samples from power traces. We develop a unique dedicated evaluation system to compare the performance of various decoders with different compression methods on real-world power traces. Our findings indicate that our proposed decoder for side-channel traces outperforms the current state of art in terms of speed, resource consumption, and accuracy. We also demonstrate our decoder’s effectiveness under resource-constrained conditions, and show that it achieves over 70% accuracy even if there are fewer than 1,000 traces in the profiling phase.

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Authors and Affiliations

  1. Department of Software and Information Systems Engineering, Ben-Gurion University, Beersheba, Israel

    Hen Hayoon & Yossi Oren

Authors
  1. Hen Hayoon
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  2. Yossi Oren
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Correspondence to Hen Hayoon or Yossi Oren .

Editor information

Editors and Affiliations

  1. Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Shlomi Dolev

  2. University of Maryland, College Park, MD, USA

    Jonathan Katz

  3. Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Amnon Meisels

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Hayoon, H., Oren, Y. (2022). Time, Memory and Accuracy Tradeoffs in Side-Channel Trace Profiling. In: Dolev, S., Katz, J., Meisels, A. (eds) Cyber Security, Cryptology, and Machine Learning. CSCML 2022. Lecture Notes in Computer Science, vol 13301. Springer, Cham. https://doi.org/10.1007/978-3-031-07689-3_3

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  • DOI: https://doi.org/10.1007/978-3-031-07689-3_3

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