Advertisement

Speed-up of SCA Attacks on 32-bit Multiplications

  • Robert Nguyen
  • Adrien Facon
  • Sylvain GuilleyEmail author
  • Guillaume Gautier
  • Safwan El Assad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11445)

Abstract

Many crypto-algorithms, Deep-Learning, DSP compute on words larger than 8-bit. SCA attacks can easily be done on Boolean operations like XOR, AND, OR, and substitution operations like s-box, p-box or q-box, as 8-bit hypothesis or less are enough to forge attacks. However, attacking larger hypothesis word increases exponentially required resources: memory and computation power. Considering multiplication, 32-bit operation implies \(2^{32}\) hypotheses. Then a direct SCA attack cannot be efficiently performed. We propose to perform instead 4 small 8-bit SCA attacks. 32-bit attack complexity is reduced to 8-bit only complexity.

Keywords

SCA Arithmetic multiplication 32-bit Divide and conquer 8-bit Reduce partition size Fault model Neural network Deep learning Signal processing PID Automotive Avionic LFSR PUF Chaotic pseudo-random generator 

References

  1. 1.
    Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999).  https://doi.org/10.1007/3-540-48405-1_25CrossRefGoogle Scholar
  2. 2.
    Assad El, S., et al.: Chaos-based block ciphers: an overview. In: IEEE 10th International Conference on Communications, COMM-2014, pp. 23–26. Romania, May, Bucharest (2014)Google Scholar
  3. 3.
    El Assad, F.: A new chaos-based image encryption system. Signal Process. Image Commun. 41, 144–157 (2016)CrossRefGoogle Scholar
  4. 4.
    Gautier, G., El Assad, S.: Design and efficient implementations of a chaos-based stream cipher for securing Internet of Things (2017)Google Scholar
  5. 5.
    Gautier, G., El Assad, S.: A promising chaos-based stream cipher (2018)Google Scholar
  6. 6.
    Batina, L., Bhasin, S., Jap, D., Picek, S.: CSI neural network - using side-channels to recover your artificial neural network information. arXiv:1810.09076v1 [cs.CR], 22 October 2018
  7. 7.
    Moellic, P.-A.: The dark side of neural networks: an advocacy for security in machine learning. J1–05. CESAR (2018)Google Scholar
  8. 8.
    Oswald, D., Paar, C.: Improving side-channel analysis with optimal pre-processing, p. 16. CARDIS (2012)Google Scholar
  9. 9.
    Bansal, H.O., Sharma, R., Shreeraman, P.R.: PID controller tuning techniques - a review. J. Control Eng. Technol JCET. 2(4), 168–176 (2012). www.vkingpub.comGoogle Scholar
  10. 10.
    Physically Unclonable Function - PUF, SR2I301. https://perso.telecom-paristech.fr/danger/SR2I301/PUF.pdf
  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).  https://doi.org/10.1007/978-3-540-28632-5_2CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Robert Nguyen
    • 1
  • Adrien Facon
    • 1
    • 3
  • Sylvain Guilley
    • 1
    • 2
    • 3
    Email author
  • Guillaume Gautier
    • 4
  • Safwan El Assad
    • 5
  1. 1.Secure-IC S.A.S. - Think Ahead Business LineCesson-SévignéFrance
  2. 2.LTCI, Telecom ParisTech, COMELEC DepartmentParisFrance
  3. 3.École Normale Supérieure Département d’InformatiqueParisFrance
  4. 4.Univ Rennes, INSA Rennes, CNRS, IETR - UMR 6164RennesFrance
  5. 5.IETR Laboratory, UMR CNRS 6164; VAADER TeamNantesFrance

Personalised recommendations