Enhancing Time-Frequency Analysis with Zero-Mean Preprocessing
Side-channel analysis is a critical threat to cryptosystems on the Internet of Things and in relation to embedded devices, and appropriate side-channel countermeasure must be required for physical security. A combined countermeasure approach employing first-order masking and desynchronization simult...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2477 |
| _version_ | 1797437789038444544 |
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| author | Sunghyun Jin Philip Johansson HeeSeok Kim Seokhie Hong |
| author_facet | Sunghyun Jin Philip Johansson HeeSeok Kim Seokhie Hong |
| author_sort | Sunghyun Jin |
| collection | DOAJ |
| description | Side-channel analysis is a critical threat to cryptosystems on the Internet of Things and in relation to embedded devices, and appropriate side-channel countermeasure must be required for physical security. A combined countermeasure approach employing first-order masking and desynchronization simultaneously is a general and cost-efficient approach to counteracting side-channel analysis. With the development of side-channel countermeasures, there are plenty of advanced attacks introduced to defeat such countermeasures. At CARDIS 2013, Belgarric et al. first proposed time-frequency analysis, a promising attack regarding the complexity of computation and memory compared to other attacks, such as conventional second-order side-channel analysis after synchronization. Nevertheless, their time-frequency analysis seems to have lower performance than expected against some datasets protected by combined countermeasures. It is therefore required to study the factors that affect the performance of time-frequency analysis. In this paper, we investigate Belgarric et al.’s time-frequency analysis and conduct a mathematical analysis in regard to the preprocessing of frequency information for second-order side-channel analysis. Based on this analysis, we claim that zero-mean preprocessing enhances the performance of time-frequency analysis. We verify that our analysis is valid through experimental results from two datasets, which are different types of first-order masked Advanced Encryption Standard (AES) software implementations. The experimental results show that time-frequency analysis with zero-mean preprocessing seems to have an enhanced or complementary performance compared to the analysis without preprocessing. |
| first_indexed | 2024-03-09T11:27:41Z |
| format | Article |
| id | doaj.art-8fbcf02c501d4edcbf4289e791811445 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:27:41Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8fbcf02c501d4edcbf4289e7918114452023-11-30T23:59:25ZengMDPI AGSensors1424-82202022-03-01227247710.3390/s22072477Enhancing Time-Frequency Analysis with Zero-Mean PreprocessingSunghyun Jin0Philip Johansson1HeeSeok Kim2Seokhie Hong3School of Cyber Security, Korea University, Seoul 02841, KoreaCenter for Information Security Technologies, Institute of Cyber Security and Privacy, Korea University, Seoul 02841, KoreaDepartment of Cyber Security, College of Science and Technology, Korea University, Sejong 30019, KoreaSchool of Cyber Security, Korea University, Seoul 02841, KoreaSide-channel analysis is a critical threat to cryptosystems on the Internet of Things and in relation to embedded devices, and appropriate side-channel countermeasure must be required for physical security. A combined countermeasure approach employing first-order masking and desynchronization simultaneously is a general and cost-efficient approach to counteracting side-channel analysis. With the development of side-channel countermeasures, there are plenty of advanced attacks introduced to defeat such countermeasures. At CARDIS 2013, Belgarric et al. first proposed time-frequency analysis, a promising attack regarding the complexity of computation and memory compared to other attacks, such as conventional second-order side-channel analysis after synchronization. Nevertheless, their time-frequency analysis seems to have lower performance than expected against some datasets protected by combined countermeasures. It is therefore required to study the factors that affect the performance of time-frequency analysis. In this paper, we investigate Belgarric et al.’s time-frequency analysis and conduct a mathematical analysis in regard to the preprocessing of frequency information for second-order side-channel analysis. Based on this analysis, we claim that zero-mean preprocessing enhances the performance of time-frequency analysis. We verify that our analysis is valid through experimental results from two datasets, which are different types of first-order masked Advanced Encryption Standard (AES) software implementations. The experimental results show that time-frequency analysis with zero-mean preprocessing seems to have an enhanced or complementary performance compared to the analysis without preprocessing.https://www.mdpi.com/1424-8220/22/7/2477second-order side-channel analysistime-frequency analysisFourier transformmaskinghidingdesynchronization |
| spellingShingle | Sunghyun Jin Philip Johansson HeeSeok Kim Seokhie Hong Enhancing Time-Frequency Analysis with Zero-Mean Preprocessing Sensors second-order side-channel analysis time-frequency analysis Fourier transform masking hiding desynchronization |
| title | Enhancing Time-Frequency Analysis with Zero-Mean Preprocessing |
| title_full | Enhancing Time-Frequency Analysis with Zero-Mean Preprocessing |
| title_fullStr | Enhancing Time-Frequency Analysis with Zero-Mean Preprocessing |
| title_full_unstemmed | Enhancing Time-Frequency Analysis with Zero-Mean Preprocessing |
| title_short | Enhancing Time-Frequency Analysis with Zero-Mean Preprocessing |
| title_sort | enhancing time frequency analysis with zero mean preprocessing |
| topic | second-order side-channel analysis time-frequency analysis Fourier transform masking hiding desynchronization |
| url | https://www.mdpi.com/1424-8220/22/7/2477 |
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