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Recent advances in deep learning-based side-channel analysis
- Jin, Sunghyun;
- Kim, Suhri;
- Kim, HeeSeok;
- Hong, Seokhie
WEB OF SCIENCE
23SCOPUS
31초록
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.
키워드
- 제목
- Recent advances in deep learning-based side-channel analysis
- 저자
- Jin, Sunghyun; Kim, Suhri; Kim, HeeSeok; Hong, Seokhie
- 발행일
- 2020-04
- 유형
- Article
- 저널명
- ETRI Journal
- 권
- 42
- 호
- 2
- 페이지
- 292 ~ 304