An Improved Template Analysis Method Based on Power Traces Preprocessing with Manifold Learning

Qingjun YUAN; An WANG; Yongjuan WANG; Tao WANG

doi:10.11999/JEIT190598

Volume 42 Issue 8

Aug. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(8): 1853-1861

Qingjun YUAN, An WANG, Yongjuan WANG, Tao WANG. An Improved Template Analysis Method Based on Power Traces Preprocessing with Manifold Learning[J]. Journal of Electronics & Information Technology, 2020, 42(8): 1853-1861. doi: 10.11999/JEIT190598

Citation:

Qingjun YUAN, An WANG, Yongjuan WANG, Tao WANG. An Improved Template Analysis Method Based on Power Traces Preprocessing with Manifold Learning[J]. Journal of Electronics & Information Technology, 2020, 42(8): 1853-1861. doi: 10.11999/JEIT190598

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An Improved Template Analysis Method Based on Power Traces Preprocessing with Manifold Learning

doi: 10.11999/JEIT190598

Qingjun YUAN^{1, 2},
An WANG³,
Yongjuan WANG^{1, 2
,
,},
Tao WANG^{1, 2}

1.
PLA Strategic Support Force Information Engineering University , Zhengzhou 450001, China
2.
Henan Key Laboratory of Network Cryptography Technology, Zhengzhou 450001, China
3.
School of Computer Science, Beijing Institute of Technology, Beijing 100081, China

Funds: The National Natural Science Foundation of China (61872040), The Fund of Henan Key Laboratory of Network Cryptography Technology (LNCT2019-S02), The National Cryptographic Development Fund of the 13th Five-Year Plan (MMJJ20170201)

Received Date: 2019-08-07
Rev Recd Date: 2019-10-31

Available Online: 2019-11-27

Publish Date: 2020-08-18

Abstract

Abstract

As the key object in the process of template analysis, power traces have the characteristics of high dimension, less effective dimension and unaligned. Before effective preprocessing, template attack is difficult to work. Based on the characteristics of energy data, a global alignment method based on manifold learning is proposed to preserve the changing characteristics of power traces, and then the dimensionality of data is reduced by linear projection. The method is validated in Panda 2018 challenge1 standard datasets respectively. The experimental results show that the feature extraction effect of this method is superior over that of traditional PCA and LDA methods. Finally, the method of template analysis is used to recover the key, and the recovery success rates can reach 80% with only two traces.
- Information security,
- Template analysis,
- Manifold learning,
- Power traces,
- Alignment algorithm,
- Dimension reduction algorithm

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References(23)

References

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