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)


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.


Principal component analysis Wavelet packet transformation Correlated power attack SM4 



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).


  1. 1.
    Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. In: Annual International Cryptology Conference, pp. 388–397. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Quisquater, J.J., Samyde, D.: Electromagnetic analysis (EMA): measures and counter-measures for smart cards. In: International Conference on Research in Smart Cards, pp. 200–210. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Kocher, P.C.: Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. In: Annual International Cryptology Conference, pp. 104–113. Springer, Heidelberg (1996)Google Scholar
  4. 4.
    Wei, Y., Wang, S., Pan, D., Zhang, L., Tingfa, X.U., Liang, S., et al.: Lexical semantic recognition for Chinese two-character words based on wavelet transform with fusion of spectrograms. J. Comput. Appl. (2017)Google Scholar
  5. 5.
    Shan, W., Wang, L., Li, Q., Guo, L., Liu, S., Zhang, Z.: A chosen-plaintext method of CPA on SM4 block cipher. In: Tenth International Conference on Computational Intelligence and Security (2014)Google Scholar
  6. 6.
    Fu, H., Bai, G., Wu, X.: Low-cost hardware implementation of SM4 based on composite field. In: Information Technology, Networking, Electronic & Automation Control Conference. IEEE (2016)Google Scholar
  7. 7.
    Chari, S., Rao, J.R., Rohatgi, P.: Template attacks. In: International Workshop on Cryptographic Hardware & Embedded Systems (2002)Google Scholar
  8. 8.
    Zhang, H., Zhou, Y., Feng, D.: Theoretical and practical aspects of multiple samples correlation power analysis. Secur. Commun. Netw. 9(18), 5166–5177 (2016)CrossRefGoogle Scholar
  9. 9.
    Mo, H.: Market-based resource allocation for energy-efficient execution of multiple concurrent applications in wireless sensor networks. In: Mobile, Ubiquitous, and Intelligent Computing (2014)Google Scholar
  10. 10.
    Huang, X., Shah, P.G., Sharma, D.: Minimizing hamming weight based on 1’s complement of binary numbers over GF (2 m). In: 2010 The 12th International Conference on Advanced Communication Technology (ICACT), vol. 2, pp. 1226–1230. IEEE (2010)Google Scholar
  11. 11.
    Sandeep, S., Rajesh, C.B.: Differential power analysis on FPGA implementation of MICKEY 128. In: IEEE International Conference on Computer Science & Information Technology (2010)Google Scholar
  12. 12.
    Guo, Z., Liu, M., Li, B.: Circuit breaker fault analysis based on wavelet packet time-frequency entropy and LM algorithm to optimize BP neural network (2018)Google Scholar
  13. 13.
    Zhou, X., Sun, D., Zhu, W., Ou, C., Ai, J.: Double-key recovery based correlation power analysis. In: Trustcom/BigDataSE/ISPA (2017)Google Scholar
  14. 14.
    Wang, Z., Wang, X., Luo, B.: Early fault feature extraction of rotor imbalance and self-healing monitoring. In: Fourth International Conference on Innovative Computing (2009)Google Scholar
  15. 15.
    Guo, Z., Dawu, G.U., Haining, L.U., Liu, J., Sen, X.U., Bao, S., et al.: A combinational power analysis method against cryptographic hardware. China Commun. 12(1), 99–107 (2015)CrossRefGoogle Scholar
  16. 16.
    Deng, S., Pei, J., Wang, Y., Liu, B.: Research on drilling mud pump fault diagnosis based on fusion of acoustic emission and vibration technology. Insight - Non-destructive Test. Condition Monit. 59(8), 415–423 (2017)CrossRefGoogle Scholar
  17. 17.
    Mestiri, H., Benhadjyoussef, N., Machhout, M., Tourki, R.: A comparative study of power consumption models for CPA attack. Int. J. Comput. Netw. Inf. Secur. 5(3), 25 (2013)Google Scholar
  18. 18.
    Moein, S., Subramnian, J., Gulliver, T.A., Gebali, F., El-Kharashi, M.W.: Classification of hardware trojan detection techniques. In: 2015 Tenth International Conference on Computer Engineering & Systems (ICCES), pp. 357–362. IEEE (2015)Google Scholar
  19. 19.
    Liu, S.C., Gao, E.G., Guo, C.S.: Seismic signal wavelet-packet denoising and fast spectrum analysis. In: Applied Mechanics and Materials, vol. 229, pp. 1772–1776. Trans Tech Publications (2012)Google Scholar
  20. 20.
    Zhou, F., Wu, N., Zhang, X., Zhang, J.: A new method for resisting collision attack based on parallel random delay S-box. IEICE Electron. Express 16(11), 20190192 (2019)CrossRefGoogle Scholar
  21. 21.
    Wu, K., Li, H., Peng, B., Yu, F.: Correlation power analysis attack against synchronous stream ciphers. In: International Conference for Young Computer Scientists (2008)Google Scholar

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

Personalised recommendations