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The Forgotten Hyperparameter:

Introducing Dilated Convolution for Boosting CNN-Based Side-Channel Attacks

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Abstract

In the evaluation of side-channel resilience, convolutional neural network-based techniques have been proved to be very effective, even in the presence of countermeasures. This work is introducing the use of dilated convolution in the context of profiling side-channel attacks. We show that the convolutional neural network that uses dilated convolution increases its performance by taking advantage of the leakage distributed through scattered points in leakage traces. We have validated the feasibility of the proposal by comparing it with the state-of-the-art approach. We have conducted experiments using ASCAD (with random key), and as a result the guessing entropy of the attack converges to zero for around 550 synchronized traces and for 3 000 desynchronised traces. In both groups of experiments, we have used the same architecture to train the model, changing just dilatation rate and kernel length, which indicates a reduction of the complexity in the deep learning model.

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Notes

  1. 1.

    i.e. intermediate value and mask leaks.

  2. 2.

    https://github.com/gabzai/Methodology-for-efficient-CNN-architectures-in-SCA.

  3. 3.

    Including One Cycle Policy to deal with the learning rate.

  4. 4.

    As well as kernel length of 3 with stride value of 6.

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Author information

Authors and Affiliations

  1. Digital Security Group, Radboud University, Nijmegen, The Netherlands

    Servio Paguada

  2. Ikerlan Technology Research Centre, Arrasate-Mondragón, Gipuzkoa, Spain

    Servio Paguada & Igor Armendariz

Authors
  1. Servio Paguada
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  2. Igor Armendariz
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Corresponding authors

Correspondence to Servio Paguada or Igor Armendariz .

Editor information

Editors and Affiliations

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

    Jianying Zhou

  2. University of Padua, Padua, Italy

    Mauro Conti

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

    Chuadhry Mujeeb Ahmed

  4. The University of Hong Kong, Hong Kong, Hong Kong

    Man Ho Au

  5. ICIS, Radboud University Nijmegen, Nijmegen, The Netherlands

    Lejla Batina

  6. University of California, Irvine, CA, USA

    Zhou Li

  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, QC, Canada

    Suryadipta Majumdar

  11. Technical University of Denmark, Lyngby, Denmark

    Weizhi Meng

  12. AppGate Inc., Bogotá, Colombia

    Martín Ochoa

  13. Delft University of Technology, Delft, The Netherlands

    Stjepan Picek

  14. Stevens Institute of Technology, Hoboken, NJ, USA

    Georgios Portokalidis

  15. City University of Hong Kong, Hong Kong, China

    Cong Wang

  16. Chinese University of Hong Kong, Shatin, Hong Kong

    Kehuan Zhang

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Paguada, S., Armendariz, I. (2020). The Forgotten Hyperparameter:. In: Zhou, J., et al. Applied Cryptography and Network Security Workshops. ACNS 2020. Lecture Notes in Computer Science(), vol 12418. Springer, Cham. https://doi.org/10.1007/978-3-030-61638-0_13

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  • DOI: https://doi.org/10.1007/978-3-030-61638-0_13

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