Recent advances in deep learning-based side-channel analysis

Sunghyun Jin, Suhri Kim, Hee Seok Kim, Seokhie Hong

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)292-304
Number of pages13
JournalETRI Journal
Issue number2
Publication statusPublished - 2020 Apr 1

Bibliographical note

Funding Information:
This research was supported by the part of Military Crypto Research Center (UD170109ED) funded by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD).

Publisher Copyright:
© 2020 ETRI


  • deep learning
  • machine learning
  • non-profiling attack
  • profiling attack
  • side-channel analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • General Computer Science
  • Electrical and Electronic Engineering


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