Research of Power Analysis Based on Ensemble Model

被引：0

作者：

Liu B. ^{[1
]}

Pan Y. ^{[1
]}

Xu S.-W. ^{[1
]}

Li J.-L. ^{[2
]}

Feng H.-M. ^{[1
,2
]}

机构：

[1] Department of Management, Beijing Electronic Science and Technology Institution, Fengtai, Beijing

[2] School of Computer Science and Technology, Xidian University, Xi'an

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2019年 / 48卷 / 02期

关键词：

Ensemble learning; KNN; Power analysis; Random forest(RF); SVM;

D O I：

10.3969/j.issn.1001-0548.2019.02.015

中图分类号：

学科分类号：

摘要：

Aiming at the problem that the single model classification algorithm has a low success rate when the number of training samples is low, an ensemble learning algorithm is presented in this paper. The experiment was conducted by applying DPA_Contest_V4 dataset. First the traditional method is used to break the mask, and then SVM, RF and kNN classification algorithms are applied to train and predict. Finally, the results of these models are combined as an ensemble model. The experimental results show that the integrated model is superior to the single model, and the success rate of the ensemble model can be about 10% higher than that of the single model especially when the number of training samples is low. © 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.

引用

页码：253 / 258

页数：5

共 10 条

[1]

Hospodar G., Gierlichs B., Mulder E.D., Et al., Machine learning in side-channel analysis: a first study, Journal of Cryptographic Engineering, 1, 4, pp. 293-302, (2011)

[2]

Markowitch O., Lerman L., Bontempi G., Side channel attack: an approach based on machine learning, International Workshop on Constructive Side-Channel Analysis and Security Design, pp. 29-41, (2011)

[3]

Lerman L., Poussier R., Bontempi G., Et al., Template attacks vs. machine learning revisited (and the curse of dimensionality in side-channel analysis), Constructive Side-Channel Analysis and Secure Design, International Workshop, Cosade 2015, pp. 20-33, (2015)

[4]

Zhou Z.-H., Machine Learning, (2016)

[5]

Bartkewitz T., Lemke-Rust K., Efficient template attacks based on probabilistic multi-class support vector machines, International Conference on Smart Card Research and Advanced Applications, pp. 263-276, (2012)

[6]

DPA Contest (4th edition)

[7]

Lerman L., Medeiros S.F., Bontempi G., Et al., A machine learning approach against a masked AES, Smart Card Research and Advanced Applications, pp. 61-75, (2013)

[8]

Zeng Z., Gu D., Liu J., Et al., An Improved side-channel attack based on support vector machine, 10th International Conference on Computational Intelligence and Security, pp. 676-680, (2014)

[9]

Deng G.-M., Zhang P., Zhao Q., Et al., Electromagnetic template analysis with PCA and SVM, Computer Measurement & Control, 17, 9, pp. 1837-1839, (2009)

[10]

Rechberger C., Oswald E., Practical template attacks, Lecture Notes in Computer Science, 3325, pp. 440-456, (2005)

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