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On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

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Applied Cryptography and Network Security Workshops (ACNS 2021)

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Abstract

In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural networks contain at least three types of layers: convolutional, pooling, and dense layers. Although the definition of pooling layers causes a large impact on neural network performance, a study on pooling hyperparameters effect on side-channel analysis is still not provided in the academic community. This paper provides extensive experimental results to demonstrate how pooling layer types and pooling stride and size affect the profiling attack performance with convolutional neural networks. Additionally, we demonstrate that pooling hyperparameters can be larger than usually used in related works and still keep good performance for profiling attacks on specific datasets.

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Authors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Lichao Wu & Guilherme Perin

Authors
  1. Lichao Wu
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  2. Guilherme Perin
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Editor information

Editors and Affiliations

  1. Singapore University of Technology and Design, Singapore, Singapore

    Jianying Zhou

  2. University of Strathclyde, Glasgow, UK

    Chuadhry Mujeeb Ahmed

  3. ICIS, Radboud University, Nijmegen, The Netherlands

    Lejla Batina

  4. Singapore University of Technology and Design, Singapore, Singapore

    Sudipta Chattopadhyay

  5. LIACS, Leiden University, Leiden, The Netherlands

    Olga Gadyatskaya

  6. Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

    Chenglu Jin

  7. University of Science and Technology of China, Hefei, China

    Jingqiang Lin

  8. University of Padua, Padua, Italy

    Eleonora Losiouk

  9. University of Kansas, Lawrence, KS, USA

    Bo Luo

  10. CIISE, Concordia University, Montréal, Canada

    Suryadipta Majumdar

  11. New York University, Abu Dhabi, United Arab Emirates

    Mihalis Maniatakos

  12. Illinois at Singapore Pte Ltd, Singapore, Singapore

    Daisuke Mashima

  13. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  14. Delft University of Technology, Delft, The Netherlands

    Stjepan Picek

  15. SECOM Co., Ltd., Tokyo, Japan

    Masaki Shimaoka

  16. University of Aizu, Aizu-Wakamatsu, Japan

    Chunhua Su

  17. City University of Hong Kong, Hong Kong, Hong Kong

    Cong Wang

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Wu, L., Perin, G. (2021). On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis. In: Zhou, J., et al. Applied Cryptography and Network Security Workshops. ACNS 2021. Lecture Notes in Computer Science(), vol 12809. Springer, Cham. https://doi.org/10.1007/978-3-030-81645-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-81645-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81644-5

  • Online ISBN: 978-3-030-81645-2

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