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http://dx.doi.org/10.13089/JKIISC.2019.29.5.997

Implementation and Analysis of Power Analysis Attack Using Multi-Layer Perceptron Method  

Kwon, Hongpil (Hoseo University)
Bae, DaeHyeon (Hoseo University)
Ha, Jaecheol (Hoseo University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.29, no.5, 2019 , pp. 997-1006 More about this Journal
Abstract
To overcome the difficulties and inefficiencies of the existing power analysis attack, we try to extract the secret key embedded in a cryptographic device using attack model based on MLP(Multi-Layer Perceptron) method. The target of our proposed power analysis attack is the AES-128 encryption module implemented on an 8-bit processor XMEGA128. We use the divide-and-conquer method in bytes to recover the whole 16 bytes secret key. As a result, the MLP-based power analysis attack can extract the secret key with the accuracy of 89.51%. Additionally, this MLP model has the 94.51% accuracy when the pre-processing method on power traces is applied. Compared to the machine leaning-based model SVM(Support Vector Machine), we show that the MLP can be a outstanding method in power analysis attacks due to excellent ability for feature extraction.
Keywords
Side-Channel Analysis; Power Analysis Attack; Deep Learning MLP; Machine Learning SVM;
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1 F. X. Standaert, B. Gierlichs, and I. Verbauwhede, "Partition vs. comparison side-channel Distinguishers : An empirical evaluation of statistical tests for univariate side-channel attacks against two unprotected CMOS device," ICISC'08, LNCS 5461, pp. 253-267, 2008.
2 S. Mangard, E. Oswald, and T. Poop, "Power analysis attacks: Revealing the secrets of smart cards," Springer, 2008.
3 F. Rosenblatt, "The perceptron: A probabilistic model for information storage and organization in the brain," Psychological Review, Vol. 65, No. 6, 1958.
4 R. Collobert and S. Benjio, "Links between perceptrons, MLPs and SVMs," Proceedings of the twenty-first international conference on Machine learning, ICML'04, p. 23, 2004.
5 Federal Information Processing Standards Publication (FIPS 197), "Advanced Encryption Standard(AES)," 2001.
6 C. Cortes, and V. Vapnik, "Support-vector networks," Machine Learning, Vol. 20, Issue 3, pp. 273-297, 1995.   DOI
7 S. Chari, J. R. Rao, and P. Rohatgi, "Template Attacks," CHES'02, LNCS 2523, pp. 13-28, 2002.
8 T. Hofmann, B. Scholkopf, and A. J. Smola, "Kernel Methods in Machine Learning," The Annals of Statistics, Vol. 36, No. 3, pp. 1171-1220, 2008.   DOI
9 P. Kocher, J. Jaffe, and B. Jun, "Differential Power Analysis," CRYPTO'99, 1999.
10 E. Brier, C. Clavier, and F. Olivier, "Correlation Power Analysis with a Leakage Model," CHES'04, LNCS 3156, pp. 16-29, 2004.
11 W. Schindler, K. Lemke, and C. Paar, "A Stochastic Model for Differential Side Channel Cryptanalysis," CHES'05, LNCS 3659, pp. 30-46, 2005.
12 Z. Martinasek, and V. Zeman, "Innovative method of the power analysis," Radioengineering, Vol. 22, No. 2, pp. 589-594, 2013.
13 Z. Martinasek, J. Hajny, and L. Malina, "Optimization of power analysis using neural network," CARDIS'13, LNCS 8419, pp. 94-107, 2014.
14 ChipWhisperer(R) - NewAE Technology Inc., "chipwhisperer," Available at http://newae.com/tools/chipwhisperer/, 2017.
15 G. Hospodar, B. Gierlichs, E. D. Mulder, I. Verbauwhede, and J. Vandewalle, "Machine learning in side-channel analysis: a first study," Journal of Cryptographic Engineering, Vol. 1, No. 4, pp.293-302, 2011.   DOI