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Convolutional Neural Network Based Side-Channel Attacks in Time-Frequency Representations

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Book cover Smart Card Research and Advanced Applications (CARDIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11389))

Abstract

Profiled attacks play a fundamental role in the evaluation of cryptographic implementation worst-case security. For the past sixteen years, great efforts have been paid to develop profiled attacks from Template Attacks to deep learning based attacks. However, most attacks are performed in time domain – may lose frequency domain information. In this paper, to utilize leakage information more effectively, we propose a novel deep learning based side-channel attack in time-frequency representations. By exploiting time-frequency patterns and extracting high level key-related features in spectrograms simultaneously, we aim to maximize the potential of convolutional neural networks in profiled attacks. Firstly, an effective network architecture is deployed to perform successful attacks. Secondly, some critical parameters in spectrogram are studied for better training the network. Moreover, we compare Template Attacks and CNN-based attacks in both time and time-frequency domain with public datasets. The heuristic results in these experiments provide a new perspective that CNN-based attacks in spectrograms give a very feasible option to the state-of-the-art profiled attacks.

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Acknowledgment

This work is supported in part by Natural Science Foundation of China (No. 61632020, 61472416 and 61602468), National Key Research and Development Program of China (No. 2017YFB0802705) and the National Cryptography Development Fund under Grant MMJJ 20180223.

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Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Guang Yang, Huizhong Li, Jingdian Ming & Yongbin Zhou

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Guang Yang, Huizhong Li, Jingdian Ming & Yongbin Zhou

Authors
  1. Guang Yang
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  2. Huizhong Li
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  3. Jingdian Ming
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  4. Yongbin Zhou
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Corresponding author

Correspondence to Yongbin Zhou .

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Editors and Affiliations

  1. Rambus - Cryptography Research, Rotterdam, Zuid-Holland, The Netherlands

    Begül Bilgin

  2. Nagravision, Cheseaux-sur-Lausanne, Vaud, Switzerland

    Jean-Bernard Fischer

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Yang, G., Li, H., Ming, J., Zhou, Y. (2019). Convolutional Neural Network Based Side-Channel Attacks in Time-Frequency Representations. In: Bilgin, B., Fischer, JB. (eds) Smart Card Research and Advanced Applications. CARDIS 2018. Lecture Notes in Computer Science(), vol 11389. Springer, Cham. https://doi.org/10.1007/978-3-030-15462-2_1

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  • DOI: https://doi.org/10.1007/978-3-030-15462-2_1

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