A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning
The leakage signals, including electromagnetic, energy, time, and temperature, generated during the operation of password devices contain highly correlated key information, which leads to security vulnerabilities. In traditional encryption algorithms, the length of the key greatly affects the upper...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/21/12025 |
| _version_ | 1797632159268208640 |
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| author | Xiaotong Cui Hongxin Zhang Xing Fang Yuanzhen Wang Danzhi Wang Fan Fan Lei Shu |
| author_facet | Xiaotong Cui Hongxin Zhang Xing Fang Yuanzhen Wang Danzhi Wang Fan Fan Lei Shu |
| author_sort | Xiaotong Cui |
| collection | DOAJ |
| description | The leakage signals, including electromagnetic, energy, time, and temperature, generated during the operation of password devices contain highly correlated key information, which leads to security vulnerabilities. In traditional encryption algorithms, the length of the key greatly affects the upper limit of its security against cracking. Regarding side-channel attacks on long-key algorithms, traditional template attack methods characterize the energy traces using multivariate Gaussian distribution during the template construction phase. The exhaustive key-guessing process is expected to consume a significant amount of time and computational resources. Therefore, to analyze the effectiveness of obtaining key values from the side information of password devices, we propose an innovative attack method based on a divide-and-conquer logical structure, targeting semi-bytes. We construct a collection of key classification submodules with symmetric correlations. By integrating a differential network model for byte-block sets and an end-to-end direct attack method, we form a holistic symmetric decision framework and propose a key classification structure based on deep transfer learning. This structure consists of three main parts: side information data acquisition, analysis of key-value effectiveness, and determination of attack positions. It employs multiple parallel symmetric subnetworks, effectively improving attack efficiency and reducing the key enumeration range. Experimental results show that the optimal attack accuracy of the network model can reach 91%, with an average attack accuracy of 78%. It overcomes overfitting issues under small sample dataset conditions. |
| first_indexed | 2024-03-11T11:33:58Z |
| format | Article |
| id | doaj.art-be7bfc422b4249b98751b063b137b1d2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T11:33:58Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-be7bfc422b4249b98751b063b137b1d22023-11-10T14:59:34ZengMDPI AGApplied Sciences2076-34172023-11-0113211202510.3390/app132112025A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer LearningXiaotong Cui0Hongxin Zhang1Xing Fang2Yuanzhen Wang3Danzhi Wang4Fan Fan5Lei Shu6School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaBeijing Microelectronics Technology Institute, Beijing 100076, ChinaBeijing Microelectronics Technology Institute, Beijing 100076, ChinaThe leakage signals, including electromagnetic, energy, time, and temperature, generated during the operation of password devices contain highly correlated key information, which leads to security vulnerabilities. In traditional encryption algorithms, the length of the key greatly affects the upper limit of its security against cracking. Regarding side-channel attacks on long-key algorithms, traditional template attack methods characterize the energy traces using multivariate Gaussian distribution during the template construction phase. The exhaustive key-guessing process is expected to consume a significant amount of time and computational resources. Therefore, to analyze the effectiveness of obtaining key values from the side information of password devices, we propose an innovative attack method based on a divide-and-conquer logical structure, targeting semi-bytes. We construct a collection of key classification submodules with symmetric correlations. By integrating a differential network model for byte-block sets and an end-to-end direct attack method, we form a holistic symmetric decision framework and propose a key classification structure based on deep transfer learning. This structure consists of three main parts: side information data acquisition, analysis of key-value effectiveness, and determination of attack positions. It employs multiple parallel symmetric subnetworks, effectively improving attack efficiency and reducing the key enumeration range. Experimental results show that the optimal attack accuracy of the network model can reach 91%, with an average attack accuracy of 78%. It overcomes overfitting issues under small sample dataset conditions.https://www.mdpi.com/2076-3417/13/21/12025side-channel attackAESsymmetrical decisiondeep learningtransfer learning |
| spellingShingle | Xiaotong Cui Hongxin Zhang Xing Fang Yuanzhen Wang Danzhi Wang Fan Fan Lei Shu A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning Applied Sciences side-channel attack AES symmetrical decision deep learning transfer learning |
| title | A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning |
| title_full | A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning |
| title_fullStr | A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning |
| title_full_unstemmed | A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning |
| title_short | A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning |
| title_sort | secret key classification framework of symmetric encryption algorithm based on deep transfer learning |
| topic | side-channel attack AES symmetrical decision deep learning transfer learning |
| url | https://www.mdpi.com/2076-3417/13/21/12025 |
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