Recent advances in deep learning-based side-channel analysis

Citations

WEB OF SCIENCE

23
Citations

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31

초록

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

deep learningmachine learningnon-profiling attackprofiling attackside-channel analysisDIFFERENTIAL POWER ANALYSISTEMPLATE ATTACKSRESISTANCEMODEL
제목
Recent advances in deep learning-based side-channel analysis
저자
Jin, SunghyunKim, SuhriKim, HeeSeokHong, Seokhie
DOI
10.4218/etrij.2019-0163
발행일
2020-04
유형
Article
저널명
ETRI Journal
42
2
페이지
292 ~ 304