Optimization of Power Analysis Using Neural Network

  • Zdenek MartinasekEmail author
  • Jan Hajny
  • Lukas Malina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8419)


In power analysis, many different statistical methods and power consumption models are used to obtain the value of a secret key from the power traces measured. An interesting method of power analysis based on multi-layer perceptron was presented in [1] claiming a \(90\,\%\) success rate. The theoretical and empirical success rates were determined to be \(80\,\%\) and \(85\,\%\), respectively, which is not sufficient enough. In the paper, we propose and realize an optimization of this power analysis method which improves the success rate to almost \(100\,\%\). The optimization is based on preprocessing the measured power traces using the calculation of the average trace and the subsequent calculation of the difference power traces. In this way, the prepared power patterns were used for neural network training and of course during the attack. This optimization is computationally undemanding compared to other methods of preprocessing usually applied in power analysis, and has a great impact on classification results. In the paper, we compare the results of the optimized method with the original implementation. We highlight positive and also some negative impacts of the optimization on classification results.


Power analysis Neural network Optimization Preprocessing 



This research work is funded by the Ministry of Industry and Trade of the Czech Republic, project FR-TI4/647. Measurements were run on computational facilities of the SIX Research Center, registration number CZ.1.05/2.1.00/03.0072.


  1. 1.
    Martinasek, Z., Zeman, V.: Innovative method of the power analysis. Radioengineering 22(2), 586–594 (2013)Google Scholar
  2. 2.
    Kocher, P.C., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  3. 3.
    Mangard, S., Oswald, E., Popp, T.: Power Analysis Attacks: Revealing the Secrets of Smart Cards (Advances in Information Security). Springer-Verlag New York Inc., Secaucus (2007)Google Scholar
  4. 4.
    Joye, M., Olivier, F.: Side-channel analysis. In: van Tilborg, H.C.A., Jajodia, S. (eds.) Encyclopedia of Cryptography and Security, pp. 1198–1204. Springer, New York (2011)Google Scholar
  5. 5.
    Chari, S., Rao, J.R., Rohatgi, P.: Template attacks. In: Kaliski, B.S., Koç, Ç.K., Paar, C. (eds.) CHES 2002. LNCS, vol. 2523, pp. 13–28. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Rechberger, C., Oswald, E.: Practical template attacks. In: Lim, C.H., Yung, M. (eds.) WISA 2004. LNCS, vol. 3325, pp. 440–456. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  7. 7.
    Hanley, N., Tunstall, M., Marnane, W.P.: Using templates to distinguish multiplications from squaring operations. Int. J. Inf. Secur. 10(4), 255–266 (2011)CrossRefGoogle Scholar
  8. 8.
    Coron, J.S., Naccache, D., Kocher, P.: Statistics and secret leakage. ACM Trans. Embed. Comput. Syst. 3(3), 492–508 (2004)CrossRefGoogle Scholar
  9. 9.
    Joye, M., Paillier, P., Schoenmakers, B.: On second-order differential power analysis. In: Rao, J.R., Sunar, B. (eds.) CHES 2005. LNCS, vol. 3659, pp. 293–308. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  10. 10.
    Herbst, C., Oswald, E., Mangard, S.: An AES smart card implementation resistant to power analysis attacks. In: Zhou, J., Yung, M., Bao, F. (eds.) ACNS 2006. LNCS, vol. 3989, pp. 239–252. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  11. 11.
    Brier, E., Clavier, C., Olivier, F.: Correlation power analysis with a leakage model. In: Joye, M., Quisquater, J.-J. (eds.) CHES 2004. LNCS, vol. 3156, pp. 16–29. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  12. 12.
    Martinasek, Z., Clupek, V., Krisztina, T.: General scheme of differential power analysis. In: 2013 36th International Conference on Telecommunications and Signal Processing (TSP), pp. 358–362 (2013)Google Scholar
  13. 13.
    Messerges, T.S., Dabbish, E.A., Sloan, R.H., Messerges, T.S., Dabbish, E.A., Sloan, R.H.: Investigations of power analysis attacks on smartcards. In: USENIX Workshop on Smartcard Technology, pp. 151–162 (1999)Google Scholar
  14. 14.
    Moradi, A., Barenghi, A., Kasper, T., Paar, C.: On the vulnerability of FPGA bitstream encryption against power analysis attacks: extracting keys from xilinx Virtex-II FPGAs. In: Proceedings of the 18th ACM Conference on Computer and Communications Security, CCS ’11, pp. 111–124. ACM, New York (2011)Google Scholar
  15. 15.
    Plos, T., Hutter, M., Feldhofer, M.: Evaluation of side-channel preprocessing techniques on cryptographic-enabled HF and UHF RFID-Tag prototypes. In: Dominikus, S. (ed.) Workshop on RFID Security 2008, Budapest, Hungary, pp. 114–127, 9–11 July 2008Google Scholar
  16. 16.
    Kasper, T., Oswald, D., Paar, C.: Side-channel analysis of cryptographic rfids with analog demodulation. In: Juels, A., Paar, C. (eds.) RFIDSec 2011. LNCS, vol. 7055, pp. 61–77. Springer, Heidelberg (2012) Google Scholar
  17. 17.
    Oswald, D., Paar, C.: Breaking Mifare DESFire MF3ICD40: power analysis and templates in the real world. In: Preneel, B., Takagi, T. (eds.) CHES 2011. LNCS, vol. 6917, pp. 207–222. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  18. 18.
    Barenghi, A., Pelosi, G., Teglia, Y.: Improving first order differential power attacks through digital signal processing. In: Proceedings of the 3rd international conference on Security of information and networks, SIN ’10, pp. 124–133. ACM (2010)Google Scholar
  19. 19.
    Kim, H.M., Kang, D.J., Kim, T.H.: Flexible key distribution for scada network using multi-agent system. In: ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security, pp. 29–34 (2007)Google Scholar
  20. 20.
    Lian, S., Sun, J., Wang, Z.: One-way hash function based on neural network. CoRR abs/0707.4032 (2007)Google Scholar
  21. 21.
    Wang, Y.H., Shen, Z.D., Zhang, H.G.: Pseudo random number generator based on hopfield neural network, pp. 2810–2813 (2006)Google Scholar
  22. 22.
    Liu, N., Guo, D.: Security analysis of public-key encryption scheme based on neural networks and its implementing. In: Wang, Y., Cheung, Y., Liu, H. (eds.) CIS 2006. LNCS (LNAI), vol. 4456, pp. 443–450. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Mislovaty, R., Perchenok, Y., Kanter, I., Kinzel, W.: Secure key-exchange protocol with an absence of injective functions. Phys. Rev. E 66, 066102 (2002)CrossRefGoogle Scholar
  24. 24.
    Fiona, A.H.Y.: ERG4920CM Thesis II Keyboard Acoustic Triangulation Attack. Ph.D. thesis, Department of Information Engineering, The Chinese University of Hong Kong (2006)Google Scholar
  25. 25.
    Zhuang, L., Zhou, F., Tygar, J.D.: Keyboard acoustic emanations revisited. In: Proceedings of the 12th ACM Conference on Computer and Communications Security, CCS ’05, pp. 373–382. ACM, New York (2005)Google Scholar
  26. 26.
    Quisquater, J.J., Samyde, D.: Automatic code recognition for smart cards using a kohonen neural network. In: Proceedings of the 5th conference on Smart Card Research and Advanced Application Conference, CARDIS’02, Berkeley, CA, USA, vol. 5, p. 6–6 (2002)Google Scholar
  27. 27.
    Kur, J., Smolka, T., Svenda, P.: Improving resiliency of java card code against power analysis. In: Mikulaska kryptobesidka, Sbornik prispevku, pp. 29–39 (2009)Google Scholar
  28. 28.
    Martinasek, Z., Macha, T., Zeman, V.: Classifier of power side channel. In: Proceedings of NIMT2010 (September 2010)Google Scholar
  29. 29.
    Yang, S., Zhou, Y., Liu, J., Chen, D.: Back propagation neural network based leakage characterization for practical security analysis of cryptographic implementations. In: Kim, H. (ed.) ICISC 2011. LNCS, vol. 7259, pp. 169–185. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  30. 30.
    Heuser, A., Zohner, M.: Intelligent machine homicide - breaking cryptographic devices using support vector machines. In: Schindler, W., Huss, S.A. (eds.) COSADE 2012. LNCS, vol. 7275, pp. 249–264. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  31. 31.
    Bartkewitz, T., Lemke-Rust, K.: Efficient template attacks based on probabilistic multi-class support vector machines. In: Mangard, S. (ed.) CARDIS 2012. LNCS, vol. 7771, pp. 263–276. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  32. 32.
    Hospodar, G., Gierlichs, B., Mulder, E.D., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptogr. Eng. 1(4), 293–302 (2011)CrossRefGoogle Scholar
  33. 33.
    Lerman, L., Bontempi, G., Markowitch, O.: Side channel attack: an approach based on machine learningn. In: COSADE 2011 - Second International Workshop on Constructive Side-Channel Analysis and Secure Design, pp. 29–41 (2011)Google Scholar
  34. 34.
    Oswald, D., Paar, C.: Improving side-channel analysis with optimal linear transforms. In: Mangard, S. (ed.) CARDIS 2012. LNCS, vol. 7771, pp. 219–233. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  35. 35.
    Martinasek, Z., Zeman, V., Sysel, P., Trasy, K.: Near electromagnetic field measurement of microprocessor. Przegl. Elektrotechniczny 89(2a), 203–207 (2013)Google Scholar
  36. 36.
    Malina, L., Clupek, V., Martinasek, Z., Hajny, J., Oguchi, K., Zeman, V.: Evaluation of software-oriented block ciphers on smartphones. In: Danger, J.-L., Debbabi, M., Marion, J.-Y., Garcia-Alfaro, J., Heywood, N.Z. (eds.) FPS 2013. LNCS, vol. 8352, pp. 353–368. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  37. 37.
    Hajny, J., Malina, L., Martinasek, Z., Tethal, O.: Performance evaluation of primitives for privacy-enhancing cryptography on current smart-cards and smart-phones. In: Garcia-Alfaro, J., Lioudakis, G., Cuppens-Boulahia, N., Foley, S., Fitzgerald, W.M. (eds.) DPM 2013 and SETOP 2013. LNCS, vol. 8247, pp. 17–33. Springer, Heidelberg (2014) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of TelecommunicationsBrno University of TechnologyBrnoCzech Republic

Personalised recommendations