Non-Profiled Deep Learning-Based Side-Channel Preprocessing With Autoencoders
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon, Donggeun | - |
dc.contributor.author | Kim, Heeseok | - |
dc.contributor.author | Hong, Seokhie | - |
dc.date.accessioned | 2021-12-07T19:41:38Z | - |
dc.date.available | 2021-12-07T19:41:38Z | - |
dc.date.created | 2021-08-30 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/130157 | - |
dc.description.abstract | In recent years, deep learning-based side-channel attacks have established their position as mainstream. However, most deep learning techniques for cryptanalysis mainly focused on classifying side-channel information in a profiled scenario where attackers can obtain a label of training data. In this paper, we introduce a novel approach with deep learning for improving side-channel attacks, especially in a non-profiling scenario. We also propose a new principle of training that trains an autoencoder through the noise from real data using noise-reduced labels. It notably diminishes the noise in measurements by modifying the autoencoder framework to the signal preprocessing. We present convincing comparisons on our custom dataset, captured from ChipWhisperer-Lite board, that demonstrate our approach outperforms conventional preprocessing methods such as principal component analysis and linear discriminant analysis. Furthermore, we apply the proposed methodology to realign de-synchronized traces that applied hiding countermeasures, and we experimentally validate the performance of the proposal. Finally, we experimentally show that we can improve the performance of higher-order side-channel attacks by using the proposed technique with domain knowledge for masking countermeasures. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Non-Profiled Deep Learning-Based Side-Channel Preprocessing With Autoencoders | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Heeseok | - |
dc.contributor.affiliatedAuthor | Hong, Seokhie | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3072653 | - |
dc.identifier.scopusid | 2-s2.0-85104267159 | - |
dc.identifier.wosid | 000641942800001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.9, pp.57692 - 57703 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 9 | - |
dc.citation.startPage | 57692 | - |
dc.citation.endPage | 57703 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | Side-channel attacks | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Performance evaluation | - |
dc.subject.keywordAuthor | Noise reduction | - |
dc.subject.keywordAuthor | Correlation | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Noise measurement | - |
dc.subject.keywordAuthor | Autoencoder | - |
dc.subject.keywordAuthor | side-channel attacks | - |
dc.subject.keywordAuthor | non-profiled | - |
dc.subject.keywordAuthor | preprocessing | - |
dc.subject.keywordAuthor | cryptography | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.