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International Conference on Applied Cryptography and Network Security

ACNS 2020: Applied Cryptography and Network Security Workshops pp 106–125Cite as

Controlling the Deep Learning-Based Side-Channel Analysis: A Way to Leverage from Heuristics

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12418))

Abstract

Deep neural networks have become the state-of-the-art method when a profiled side-channel analysis is performed. Their popularity is mostly due to neural nets overcoming some of the drawbacks of “classical” sidßhe need for feature selection or waveform synchronization, in addition to their capability to bypass certain countermeasures like random delays. To design and tune a neural network for side-channel analysis systematically is a complicated task. There exist hyperparameter tuning techniques which can be used in the side-channel analysis context, like Grid Search, but they are not optimal since they usually rely on specific machine learning metrics that cannot be directly linked to e.g. the success of the attack.

We propose a customized version of an existing statistical methodology called Six Sigma for optimizing the deep learning-based side-channel analysis process. We demonstrate the proposed methodology by successfully attacking a masked software implementation of AES.

S. Paguada and U. Rioja—These authors contributed equally to this work.

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Notes

  1. 1.

    The key guessing vector \(\mathbf{g} \) (over \(n_{a}\) power traces) is computed using the log-likelihood principle \( g_{i}=\sum _{j=1}^{n_{a}} \log \left( \hat{p}_{i j}\right) \).

  2. 2.

    In SCA on software AES implementations, is common to target 8-bit intermediate values. In this case, since the size of the keyspace |K| is \(2^8\), the maximum GE value (worst case) is 256.

  3. 3.

    In [48], if we analyze the possible combinations of the specified subset of hyperparameters for Grid search optimization, we obtain \( 3^{2} \cdot 4^{2} \cdot 8^{1} \cdot 7^{1} \cdot 5^{1} = 40\,320 \) possible combinations

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Author information

Authors and Affiliations

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

    Servio Paguada & Unai Rioja

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

    Servio Paguada, Unai Rioja & Igor Armendariz

Authors
  1. Servio Paguada
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  2. Unai Rioja
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  3. Igor Armendariz
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Corresponding authors

Correspondence to Servio Paguada , Unai Rioja 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., Rioja, U., Armendariz, I. (2020). Controlling the Deep Learning-Based Side-Channel Analysis: A Way to Leverage from Heuristics. 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_7

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  • DOI: https://doi.org/10.1007/978-3-030-61638-0_7

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