Challenges in Deep Learning-Based Profiled Side-Channel Analysis

  • Stjepan PicekEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11947)


In recent years, profiled side-channel attacks based on machine learning proved to be very successful in breaking cryptographic implementations in various settings. Still, despite successful attacks even in the presence of countermeasures, there are many open questions. A large part of the research concentrates on improving the performance of attacks while little is done to understand them and even more importantly, use that knowledge in the design of more secure implementations. In this paper, we start by briefly recollecting on the state-of-the-art in machine learning-based side-channel analysis. Afterward, we discuss several challenges we believe will play an important role in future research.


  1. 1.
    Bhasin, S., Chattopadhyay, A., Heuser, A., Jap, D., Picek, S., Shrivastwa, R.R.: Mind the portability: a warriors guide through realistic profiled side-channel analysis. Cryptology ePrint Archive, Report 2019/661 (2019).
  2. 2.
    Cagli, E., Dumas, C., Prouff, E.: Convolutional neural networks with data augmentation against jitter-based countermeasures. In: Fischer, W., Homma, N. (eds.) CHES 2017. LNCS, vol. 10529, pp. 45–68. Springer, Cham (2017). Scholar
  3. 3.
    Chari, S., Rao, J.R., Rohatgi, P.: Template attacks. In: Kaliski, B.S., Koç, K., Paar, C. (eds.) CHES 2002. LNCS, vol. 2523, pp. 13–28. Springer, Heidelberg (2003). Scholar
  4. 4.
    Choudary, O., Kuhn, M.G.: Efficient template attacks. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 253–270. Springer, Cham (2014). Scholar
  5. 5.
    Das, D., Golder, A., Danial, J., Ghosh, S., Raychowdhury, A., Sen, S.: X-DeepSCA: cross-device deep learning side channel attack. In: Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019, pp. 134:1–134:6. ACM, New York (2019)Google Scholar
  6. 6.
    Gilmore, R., Hanley, N., O’Neill, M.: Neural network based attack on a masked implementation of AES. In: 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 106–111, May 2015Google Scholar
  7. 7.
    Hospodar, G., De Mulder, E., Gierlichs, B.: Least squares support vector machines for side-channel analysis. Center for Advanced Security Research Darmstadt, pp. 99–104, January 2011Google Scholar
  8. 8.
    Kim, J., Picek, S., Heuser, A., Bhasin, S., Hanjalic, A.: Make some noise. Unleashing the power of convolutional neural networks for profiled side-channel analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2019(3), 148–179 (2019)Google Scholar
  9. 9.
    Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999). Scholar
  10. 10.
    Lerman, L., Medeiros, S.F., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 61–75. Springer, Cham (2014). Scholar
  11. 11.
    Maghrebi, H., Portigliatti, T., Prouff, E.: Breaking cryptographic implementations using deep learning techniques. In: Carlet, C., Hasan, M.A., Saraswat, V. (eds.) SPACE 2016. LNCS, vol. 10076, pp. 3–26. Springer, Cham (2016). Scholar
  12. 12.
    Mangard, S., Oswald, E., Popp, T.: Power Analysis Attacks: Revealing the Secrets of Smart Cards. Springer, New York (2006). ISBN 0-387-30857-1. Scholar
  13. 13.
    Martinasek, Z., Zeman, V.: Innovative method of the power analysis. Radioengineering 22(2), 586–594 (2013)Google Scholar
  14. 14.
    Masure, L., Dumas, C., Prouff, E.: A comprehensive study of deep learning for side-channel analysis. Cryptology ePrint Archive, Report 2019/439 (2019).
  15. 15.
    Picek, S., Heuser, A., Guilley, S.: Template attack versus Bayes classifier. J. Cryptogr. Eng. 7(4), 343–351 (2017)CrossRefGoogle Scholar
  16. 16.
    Picek, S., Heuser, A., Guilley, S.: Profiling side-channel analysis in the restricted attacker framework. Cryptology ePrint Archive, Report 2019/168 (2019).
  17. 17.
    Picek, S., Heuser, A., Jovic, A., Bhasin, S., Regazzoni, F.: The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2019(1), 209–237 (2019)Google Scholar
  18. 18.
    Picek, S., Samiotis, I.P., Kim, J., Heuser, A., Bhasin, S., Legay, A.: On the performance of convolutional neural networks for side-channel analysis. In: Chattopadhyay, A., Rebeiro, C., Yarom, Y. (eds.) SPACE 2018. LNCS, vol. 11348, pp. 157–176. Springer, Cham (2018). Scholar
  19. 19.
    Quisquater, J.-J., Samyde, D.: ElectroMagnetic analysis (EMA): measures and counter-measures for smart cards. In: Attali, I., Jensen, T. (eds.) E-smart 2001. LNCS, vol. 2140, pp. 200–210. Springer, Heidelberg (2001). Scholar
  20. 20.
    Schindler, W., Lemke, K., Paar, C.: A stochastic model for differential side channel cryptanalysis. In: Rao, J.R., Sunar, B. (eds.) CHES 2005. LNCS, vol. 3659, pp. 30–46. Springer, Heidelberg (2005). Scholar
  21. 21.
    van der Valk, D., Picek, S.: Bias-variance decomposition in machine learning-based side-channel analysis. Cryptology ePrint Archive, Report 2019/570 (2019).
  22. 22.
    Zaid, G., Bossuet, L., Habrard, A., Venelli, A.: Methodology for efficient CNN architectures in profiling attacks. Cryptology ePrint Archive, Report 2019/803 (2019).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Delft University of TechnologyDelftThe Netherlands

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