Recent advances in deep learning-based side-channel analysis

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
JournalETRI Journal
DOIs
Publication statusAccepted/In press - 2020 Jan 1

Keywords

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

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Science(all)
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Recent advances in deep learning-based side-channel analysis'. Together they form a unique fingerprint.

Cite this