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Power Consumption Attack Based on Improved Principal Component Analysis

  • Zeyu Wang
  • Wei ZhangEmail author
  • Peng Ma
  • Xu An Wang
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 97)

Abstract

Accompanied with the status quo and problems that the low efficiency in the traditional methods of principal component analysis (PCA) when we face the problems of correlated power attack with large amount of data, we presents an improved method to reduce the noise of power data by wavelet packet transform (WPT) and then reduce the dimension by traditional principal component analysis, based the conclusion we have arrived about the advantage of wavelet packet transform in signal processing. It is more productive than common methods in the data processing phase of the related power attack, especially on the occasion that we own high dimensional data with low signal to Noise Ratio (SNR). Just to show you where we can optimize, the middle position of SM4 encryption algorithm was selected to measure the power consumption, and compared with the results of traditional principal component analysis. The results show that not only is the number of curves has been significantly reduced, but the computational complexity has been decreased easily, by all means, the computational time is less than the original required time so that the attack efficiency is significantly improved. Aiming at the goal with a highly targeted way to reduce the amount of data which are needed to crack the key especially for course of power analysis, the proposal submitted by us have the certain advantages under this circumstance when we face the high latitude data with low SNR within the process of correlated power attack.

Keywords

Principal component analysis Wavelet packet transformation Correlated power attack SM4 

Notes

Acknowledgements

This work is supported by the National Key Research and Development Program of China Under Grants No. 2017YFB0802000, National Cryptography Development Fund of China Under Grants No. MMJJ20170112, the Natural Science Basic Research Plan in Shaanxi Province of china (Grant Nos. 2018JM6028), National Nature Science Foundation of China (Grant Nos. 61772550, 61572521, U1636114, 61402531), Engineering University of PAP’s Funding for Scientific Research Innovation Team (grant no. KYTD201805).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Key Laboratory of Network and Information Security under Chinese People Armed Police Force (PAP)Engineering University of PAPXi’anChina

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