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Recent advances in deep learning-based side-channel analysis

  • Jin, Sunghyun (School of Cyber Security, Korea University) ;
  • Kim, Suhri (School of Cyber Security, Korea University) ;
  • Kim, HeeSeok (Department of Information Security, College of Science and Technology, Korea University) ;
  • Hong, Seokhie (School of Cyber Security, Korea University)
  • Received : 2019.03.28
  • Accepted : 2019.07.29
  • Published : 2020.04.03
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Abstract

As side-channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side-channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning-based side-channel analysis. In particular, we outline how deep learning is applied to side-channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

Keywords

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