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Challenges in Deep Learning-Based Profiled Side-Channel Analysis

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Security, Privacy, and Applied Cryptography Engineering (SPACE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11947))

Abstract

In recent years, profiled side-channel attacks based on machine learning proved to be very successful in breaking cryptographic implementations in various settings. Still, despite successful attacks even in the presence of countermeasures, there are many open questions. A large part of the research concentrates on improving the performance of attacks while little is done to understand them and even more importantly, use that knowledge in the design of more secure implementations. In this paper, we start by briefly recollecting on the state-of-the-art in machine learning-based side-channel analysis. Afterward, we discuss several challenges we believe will play an important role in future research.

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Authors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Stjepan Picek

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  1. Stjepan Picek
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Correspondence to Stjepan Picek .

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Editors and Affiliations

  1. Temasek Laboratories, Nanyang Technological University, Singapore, Singapore

    Shivam Bhasin

  2. Computer Science Department, Technion, Haifa, Israel

    Avi Mendelson

  3. Indian Statistical Institute, Kolkata, India

    Mridul Nandi

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Picek, S. (2019). Challenges in Deep Learning-Based Profiled Side-Channel Analysis. In: Bhasin, S., Mendelson, A., Nandi, M. (eds) Security, Privacy, and Applied Cryptography Engineering. SPACE 2019. Lecture Notes in Computer Science(), vol 11947. Springer, Cham. https://doi.org/10.1007/978-3-030-35869-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-35869-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35868-6

  • Online ISBN: 978-3-030-35869-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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