Recent advances in deep learning‐based side‐channel analysis
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Electronics and Telecommunications Research Institute (ETRI)
2020-02-01
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| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2019-0163 |
| _version_ | 1819227672492900352 |
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| author | Sunghyun Jin Suhri Kim HeeSeok Kim Seokhie Hong |
| author_facet | Sunghyun Jin Suhri Kim HeeSeok Kim Seokhie Hong |
| author_sort | Sunghyun Jin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T10:45:06Z |
| format | Article |
| id | doaj.art-526a157537904085b74a407785ee979b |
| institution | Directory Open Access Journal |
| issn | 1225-6463 |
| language | English |
| last_indexed | 2024-12-23T10:45:06Z |
| publishDate | 2020-02-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj.art-526a157537904085b74a407785ee979b2022-12-21T17:50:04ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632020-02-0142229230410.4218/etrij.2019-016310.4218/etrij.2019-0163Recent advances in deep learning‐based side‐channel analysisSunghyun JinSuhri KimHeeSeok KimSeokhie HongAs 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.https://doi.org/10.4218/etrij.2019-0163deep learningmachine learningnon‐profiling attackprofiling attackside‐channel analysis |
| spellingShingle | Sunghyun Jin Suhri Kim HeeSeok Kim Seokhie Hong Recent advances in deep learning‐based side‐channel analysis ETRI Journal deep learning machine learning non‐profiling attack profiling attack side‐channel analysis |
| title | Recent advances in deep learning‐based side‐channel analysis |
| title_full | Recent advances in deep learning‐based side‐channel analysis |
| title_fullStr | Recent advances in deep learning‐based side‐channel analysis |
| title_full_unstemmed | Recent advances in deep learning‐based side‐channel analysis |
| title_short | Recent advances in deep learning‐based side‐channel analysis |
| title_sort | recent advances in deep learning based side channel analysis |
| topic | deep learning machine learning non‐profiling attack profiling attack side‐channel analysis |
| url | https://doi.org/10.4218/etrij.2019-0163 |
| work_keys_str_mv | AT sunghyunjin recentadvancesindeeplearningbasedsidechannelanalysis AT suhrikim recentadvancesindeeplearningbasedsidechannelanalysis AT heeseokkim recentadvancesindeeplearningbasedsidechannelanalysis AT seokhiehong recentadvancesindeeplearningbasedsidechannelanalysis |