On the performance of non‐profiled side channel attacks based on deep learning techniques
Abstract In modern embedded systems, security issues including side‐channel attacks (SCAs) are becoming of paramount importance since the embedded devices are ubiquitous in many categories of consumer electronics. Recently, deep learning (DL) has been introduced as a new promising approach for profi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Hindawi-IET
2023-05-01
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| Series: | IET Information Security |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ise2.12102 |
| _version_ | 1797422338878210048 |
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| author | Ngoc‐Tuan Do Van‐Phuc Hoang Van Sang Doan Cong‐Kha Pham |
| author_facet | Ngoc‐Tuan Do Van‐Phuc Hoang Van Sang Doan Cong‐Kha Pham |
| author_sort | Ngoc‐Tuan Do |
| collection | DOAJ |
| description | Abstract In modern embedded systems, security issues including side‐channel attacks (SCAs) are becoming of paramount importance since the embedded devices are ubiquitous in many categories of consumer electronics. Recently, deep learning (DL) has been introduced as a new promising approach for profiled and non‐profiled SCAs. This paper proposes and evaluates the applications of different DL techniques including the Convolutional Neural Network and the multilayer perceptron models for non‐profiled attacks on the AES‐128 encryption implementation. Especially, the proposed network is fine‐tuned with different number of hidden layers, labelling techniques and activation functions. Along with the designed models, a dataset reconstruction and labelling technique for the proposed model has also been performed for solving the high dimension data and imbalanced dataset problem. As a result, the DL based SCA with our reconstructed dataset for different targets of ASCAD, RISC‐V microcontroller, and ChipWhisperer boards has achieved a higher performance of non‐profiled attacks. Specifically, necessary investigations to evaluate the efficiency of the proposed techniques against different SCA countermeasures, such as masking and hiding, have been performed. In addition, the effect of the activation function on the proposed DL models was investigated. The experimental results have clarified that the exponential linear unit function is better than the rectified linear unit in fighting against noise generation‐based hiding countermeasure. |
| first_indexed | 2024-03-09T07:30:52Z |
| format | Article |
| id | doaj.art-0198f9f51d0b4730b284287cfe551d2a |
| institution | Directory Open Access Journal |
| issn | 1751-8709 1751-8717 |
| language | English |
| last_indexed | 2024-03-09T07:30:52Z |
| publishDate | 2023-05-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Information Security |
| spelling | doaj.art-0198f9f51d0b4730b284287cfe551d2a2023-12-03T06:14:31ZengHindawi-IETIET Information Security1751-87091751-87172023-05-0117337739310.1049/ise2.12102On the performance of non‐profiled side channel attacks based on deep learning techniquesNgoc‐Tuan Do0Van‐Phuc Hoang1Van Sang Doan2Cong‐Kha Pham3Institute of System Integration Le Quy Don Technical University Hanoi VietnamInstitute of System Integration Le Quy Don Technical University Hanoi VietnamVietnam Naval Academy Nha Trang VietnamThe University of Electro‐Communications (UEC) Tokyo JapanAbstract In modern embedded systems, security issues including side‐channel attacks (SCAs) are becoming of paramount importance since the embedded devices are ubiquitous in many categories of consumer electronics. Recently, deep learning (DL) has been introduced as a new promising approach for profiled and non‐profiled SCAs. This paper proposes and evaluates the applications of different DL techniques including the Convolutional Neural Network and the multilayer perceptron models for non‐profiled attacks on the AES‐128 encryption implementation. Especially, the proposed network is fine‐tuned with different number of hidden layers, labelling techniques and activation functions. Along with the designed models, a dataset reconstruction and labelling technique for the proposed model has also been performed for solving the high dimension data and imbalanced dataset problem. As a result, the DL based SCA with our reconstructed dataset for different targets of ASCAD, RISC‐V microcontroller, and ChipWhisperer boards has achieved a higher performance of non‐profiled attacks. Specifically, necessary investigations to evaluate the efficiency of the proposed techniques against different SCA countermeasures, such as masking and hiding, have been performed. In addition, the effect of the activation function on the proposed DL models was investigated. The experimental results have clarified that the exponential linear unit function is better than the rectified linear unit in fighting against noise generation‐based hiding countermeasure.https://doi.org/10.1049/ise2.12102computer network securitycryptographyembedded systemssecurity |
| spellingShingle | Ngoc‐Tuan Do Van‐Phuc Hoang Van Sang Doan Cong‐Kha Pham On the performance of non‐profiled side channel attacks based on deep learning techniques IET Information Security computer network security cryptography embedded systems security |
| title | On the performance of non‐profiled side channel attacks based on deep learning techniques |
| title_full | On the performance of non‐profiled side channel attacks based on deep learning techniques |
| title_fullStr | On the performance of non‐profiled side channel attacks based on deep learning techniques |
| title_full_unstemmed | On the performance of non‐profiled side channel attacks based on deep learning techniques |
| title_short | On the performance of non‐profiled side channel attacks based on deep learning techniques |
| title_sort | on the performance of non profiled side channel attacks based on deep learning techniques |
| topic | computer network security cryptography embedded systems security |
| url | https://doi.org/10.1049/ise2.12102 |
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