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Recent advances in deep learning-based side-channel analysis

Authors
Jin, SunghyunKim, SuhriKim, HeeSeokHong, Seokhie
Issue Date
4월-2020
Publisher
WILEY
Keywords
deep learning; machine learning; non-profiling attack; profiling attack; side-channel analysis
Citation
ETRI JOURNAL, v.42, no.2, pp.292 - 304
Indexed
SCIE
SCOPUS
KCI
Journal Title
ETRI JOURNAL
Volume
42
Number
2
Start Page
292
End Page
304
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/56801
DOI
10.4218/etrij.2019-0163
ISSN
1225-6463
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.
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