Multiple S-Box Correlation Energy Analysis Model Based on Particle Swarm Optimization

  • Wu-jun YaoEmail author
  • Hai-bin Yang
  • Lin Chen
  • Bin Wei
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


Aiming at the problem that the amount of calculation of correlation energy analysis is too large in the process of attacking multiple S-box corresponding keys, this paper proposes a multiple S-box correlation energy analysis model based on particle swarm optimization. The particle swarm optimization algorithm has the characteristics of simple structure, fast search speed and memory, therefore, our model simultaneously attacks multiple S-boxes, which can reduce the amount of calculation, thereby achieving the goal of recovering the key efficiently and correctly. Finally, experimental analysis and verification results of the DES algorithm indicate that our new energy analysis model has about 55% improvement in efficiency and 30% improvement in accuracy over traditional energy analysis models.


  1. 1.
    Brier, E., Clavier, C., Olivier, F.: Correlation power analysis with a leakage model. In: Cryptographic Hardware and Embedded Systems-CHES 2004 (2004)Google Scholar
  2. 2.
    Li, H., He, G., Guo, Q.: Similarity retrieval method of organic mass spectrometry based on the Pearson correlation coefficient. Chem. Anal. Meterage 24(3), 33–37 (2015)Google Scholar
  3. 3.
    Mizuno, H., Iwai, K., Tanaka, H., Kurokawa, T.: A correlation power analysis countermeasure for Enocoro-128 v2 using random switching logic. In: 2012 Third International Conference on Networking and Computing (ICNC) (2012)Google Scholar
  4. 4.
    Zhang, Z., Wu, L., Wang, A., et al.: Improved Leakage Model Based on Genetic Algorithm. IACR Cryptology EPrint Archive, 2014: 314 (2014)Google Scholar
  5. 5.
    Nakai, T., Shibatani, M., Shiozaki, M., Kubota, T., Fujino, T.: Side-channel attack resistant AES cryptographic circuits with ROM reducing address-dependent EM leaks. In: 2014 IEEE International Symposium on Circuits and Systems (ISCAS) (2014)Google Scholar
  6. 6.
    Qiu, W.-X., Xiao, K.-Z., Ni, F., Huang, H.: DES key extension method. Comput. Eng. 37(5), 167–168+171 (2011)Google Scholar
  7. 7.
    Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: Science and Information (2014)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.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE (1995)Google Scholar
  10. 10.
    Pathak, V.K., Singh, A.K., Singh, R., Chaudhary, H.: A modified algorithm of particle swarm optimization for form error evaluation. tm-Tech. Mess. 84(4), 272–292 (2017)Google Scholar
  11. 11.
    Huang, W.-X.: Research on the development of particle swarm optimization. Comput. Eng. Softw. 35(4), 73–77 (2014)Google Scholar
  12. 12.
    Hemanth, D.J., Umamaheswari, S., Popescu, D.E., Naaji, A.: Application of genetic algorithm and particle swarm optimization techniques for improved image steganography systems. Open Phys. 14(1), 452–462 (2016)Google Scholar
  13. 13.
    Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation (1997)Google Scholar
  14. 14.
    Hu, M.-Y., Liu, R.-H.: Analysis and research on security of DES algorithm. Acta Sci. Nat. Univ. NeiMongol 6, 95–99 (2005)Google Scholar
  15. 15.
    Pan, Q., Zhang, L., Dai, G., et al.: Two denoising methods by wavelet transform. IEEE Trans. Signal Process. 47(12), 3401–3406 (1999)CrossRefGoogle Scholar
  16. 16.
    Lopez, M.C., Fabregas, X.: Polarimetric SAR speckle noise model. IEEE Trans. Geosci. Remote Sens. 41(10), 2232–2242 (2003)CrossRefGoogle Scholar
  17. 17.
    Wang, D.-F., Meng, L.: Performance analysis and parameter selection of PSO algorithm. Acta Autom. Sin. 42(10), 1552–1561 (2016)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Duan, X.-D., Gao, H.-X., Zhang, X.-D., Liu, X.-D.: Relations between population structure and population diversity of particle swarm optimization algorithm. Comput. Sci. 34(11), 164–166 (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Cryptographic EngineeringEngineering University of the Chinese People’s Armed PoliceXi’anChina

Personalised recommendations