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Traditional Machine Learning Methods for 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

Traditional machine learning techniques (excluding deep learning) include a range of approaches, such as supervised, semi-supervised, and unsupervised modeling methods, often coupled with data augmentation and dimensionality reduction. The aim of this chapter is to provide an overview of the application of traditional machine learning methods in the field of side-channel analysis. The chapter encompasses the common methods used in side-channel attacks, a historical overview of the use of machine learning methods in side-channel analysis, and a brief description of various machine learning approaches that have been used in related studies. Both machine learning methods and side-channel specific methods such as Principal Component Analysis, Linear Discriminant Analysis, Template Attacks, Random Forests, Multilayer Perceptron and many others are compared and the current status of their use in side-channel analysis is presented. Several research avenues are still incomplete and the chapter points out some of the open questions.

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Notes

  1. 1.

    Data masking is the process of hiding original data by altering its content. It is based on the simple idea that the message and the key are masked with a randomly generated mask at the beginning of the computation, after which the rest is performed as if there were no mask. At the end, the mask must be known so that the original data can be recovered.

  2. 2.

    It is possible to perform SCA with power, electromagnetic, acoustic, or other signal types, see Table 1, but in this chapter, for simplicity and because they are common, we will consider only power signals. Similar models can be described for other signal types.

  3. 3.

    Multivariate SCA considers multiple time points of the measured traces when building the model.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Alan Jovic

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

    Dirmanto Jap

  3. Fontys University of Applied Sciences, Eindhoven, Netherlands

    Louiza Papachristodoulou

  4. French National Center for Scientific Research (CNRS), IRISA, Rennes, France

    Annelie Heuser

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  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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Jovic, A., Jap, D., Papachristodoulou, L., Heuser, A. (2022). Traditional Machine Learning Methods for 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_2

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