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Deep Learning on Side-Channel Analysis

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Security and Artificial Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13049))

Abstract

This chapter provides an overview of recent applications of deep learning to profiled side-channel analysis (SCA). The advent of deep neural networks (mainly multiple layer perceptrons and convolutional neural networks) as a learning algorithm for profiled SCA opened several new directions and possibilities to explore the occurrence of side-channel leakages from different categories of systems. This is particularly important for designers to verify to what extent an adversary can extract sensitive information when possessing state-of-the-art attack methods. Deep learning is a fast-evolving technology that provides several advantages in profiled SCA and we summarize what are the main directions and results obtained by the research community.

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Authors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Marina Krček, Huimin Li, Lichao Wu & Guilherme Perin

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

    Servio Paguada, Unai Rioja & Łukasz Chmielewski

  3. Riscure, Delft, The Netherlands

    Łukasz Chmielewski

  4. Ikerlan Technological Research Centre, Arrasate, Spain

    Servio Paguada & Unai Rioja

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  1. Marina Krček
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  2. Huimin Li
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  3. Servio Paguada
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  4. Unai Rioja
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  5. Lichao Wu
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  6. Guilherme Perin
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  7. Łukasz Chmielewski
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Correspondence to Łukasz Chmielewski .

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Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Lejla Batina

  2. Leiden University, Leiden, The Netherlands

    Thomas Bäck

  3. Radboud University Nijmegen, Nijmegen, The Netherlands

    Ileana Buhan

  4. Radboud University Nijmegen, Nijmegen, The Netherlands

    Stjepan Picek

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Krček, M. et al. (2022). Deep Learning on Side-Channel Analysis. In: Batina, L., Bäck, T., Buhan, I., Picek, S. (eds) Security and Artificial Intelligence. Lecture Notes in Computer Science, vol 13049. Springer, Cham. https://doi.org/10.1007/978-3-030-98795-4_3

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