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

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Security, Privacy, and Applied Cryptography Engineering (SPACE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,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.

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Author information

Authors and Affiliations

  1. Delft University of Technology, Mekelweg 2, Delft, The Netherlands

    Stjepan Picek, Ioannis Petros Samiotis & Jaehun Kim

  2. CNRS, IRISA, Rennes, France

    Annelie Heuser

  3. Physical Analysis and Cryptographic Engineering, Temasek Laboratories, Nanyang Technological University, Singapore, Singapore

    Shivam Bhasin

  4. Inria, IRISA, Rennes, France

    Axel Legay

Authors
  1. Stjepan Picek
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  2. Ioannis Petros Samiotis
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  3. Jaehun Kim
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  4. Annelie Heuser
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  5. Shivam Bhasin
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  6. Axel Legay
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Corresponding author

Correspondence to Shivam Bhasin .

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Editors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Anupam Chattopadhyay

  2. Indian Institute of Technology Madras, Chennai, India

    Chester Rebeiro

  3. University of Adelaide, Adelaide, Australia

    Yuval Yarom

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Picek, S., Samiotis, I.P., Kim, J., Heuser, A., Bhasin, S., Legay, A. (2018). On the Performance of Convolutional Neural Networks for Side-Channel Analysis. In: Chattopadhyay, A., Rebeiro, C., Yarom, Y. (eds) Security, Privacy, and Applied Cryptography Engineering. SPACE 2018. Lecture Notes in Computer Science(), vol 11348. Springer, Cham. https://doi.org/10.1007/978-3-030-05072-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-05072-6_10

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  • Online ISBN: 978-3-030-05072-6

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