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Template attack versus Bayes classifier

  • Special Section on Proofs 2016
  • Published:
Journal of Cryptographic Engineering Aims and scope Submit manuscript

Abstract

Side-channel attacks represent one of the most powerful categories of attacks on cryptographic devices with profiled attacks in a prominent place as the most powerful among them. Indeed, for instance, template attack is a well-known real-world attack that is also the most powerful attack from the information theoretical perspective. On the other hand, machine learning techniques have proved their quality in a numerous applications where one is definitely side-channel analysis. As one could expect, most of the research concerning supervised machine learning and side-channel analyses concentrated on more powerful machine learning techniques. Although valid from the practical perspective, such attacks often remain lacking from the more theoretical side. In this paper, we investigate several Bayes classifiers, which present simple supervised techniques that have significant similarities with the template attack. More specifically, our analysis aims to investigate what is the influence of the feature (in)dependence in datasets with different amount of noise and to offer further insight into the efficiency of machine learning for side-channel analysis.

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Notes

  1. See, e.g., in the hall of fame on [22]

References

  1. Kocher, P.C.: Timing attacks on implementations of Diffie–Hellman, RSA, DSS, and other systems. In: Proceedings of CRYPTO’96. LNCS, vol. 1109, pp. 104–113. Springer (1996)

  2. Kocher, P.C., Jaffe, J., Jun, B.: Differential power analysis. In: Proceedings of CRYPTO’99. LNCS, vol. 1666, pp. 388–397. Springer (1999)

  3. Gandolfi, K., Mourtel, C., Olivier, F.: Electromagnetic analysis: concrete results. In: Proceedings of the Third International Workshop on Cryptographic Hardware and Embedded Systems. CHES ’01, pp. 251–261. Springer, London (2001)

  4. Chari, S., Rao, J.R., Rohatgi, P.: Template attacks. In: CHES. LNCS, vol. 2523, pp. 13–28. Springer, San Francisco Bay (Redwood City) (2002)

  5. Heuser, A., Zohner, M.: Intelligent machine homicide—breaking cryptographic devices using support vector machines. In: Schindler, W., Huss, S.A., (eds.) COSADE. LNCS, vol. 7275, pp. 249–264. Springer (2012)

  6. Hospodar, G., Gierlichs, B., De Mulder, E., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptogr. Eng. 1, 293–302 (2011)

    Article  Google Scholar 

  7. Lerman, L., Bontempi, G., Markowitch, O.: Power analysis attack: an approach based on machine learning. Int. J. Appl. Cryptol. 3(2), 97–115 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  8. Lerman, L., Poussier, R., Bontempi, G., Markowitch, O., Standaert, F.: Template attacks vs. machine learning revisited (and the curse of dimensionality in side-channel analysis). In: Mangard, S., Poschmann, A.Y., (eds.) Constructive Side-Channel Analysis and Secure Design—6th International Workshop, COSADE 2015, 13–14 Apr 2015. Revised selected papers, Lecture notes in computer science, vol. 9064, pp. 20–33. Springer, Berlin (2015)

  9. Lerman, L., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES—reaching the limit of side-channel attacks with a learning model. J. Cryptogr. Eng. 5(2), 123–139 (2015)

    Article  Google Scholar 

  10. Lerman, L., Medeiros, S.F., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES. In: CARDIS. Lecture Notes in Computer Science, Springer, Berlin (2013)

  11. Heuser, A., Picek, S., Guilley, S., Mentens, N.: Side-channel analysis of lightweight ciphers: does lightweight equal easy? In: RFIDSec. (2016)

  12. Maghrebi, H., Portigliatti, T., Prouff, E.: Breaking cryptographic implementations using deep learning techniques. In: International Conference on Security, Privacy, and Applied Cryptography Engineering, pp. 3–26. Springer (2016)

  13. Picek, S., Heuser, A., Jovic, A., Legay, A.: Climbing down the hierarchy: hierarchical classification for machine learning side-channel attacks, In: International Conference on Cryptology in Africa. pp. 61–78 (2017)

  14. Picek, S., Heuser, A., Jović, A., Ludwig, S.A., Guilley, S., Jakobović, D., Mentens, N.: Side-channel analysis and machine learning: a practical perspective. In: IEEE International Joint Conference on Neural Networks (IJCNN). pp. 4095–4102 (2017)

  15. Wolpert, D.H.: The lack of a priori distinctions between learning algorithms. Neural Comput. 8(7), 1341–1390 (1996)

    Article  Google Scholar 

  16. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997)

    Article  MATH  Google Scholar 

  17. Choudary, O., Kuhn, M.G.: Efficient template attacks. In: Francillon, A., Rohatgi, P., (eds.) Smart Card Research and Advanced Applications—12th International Conference, CARDIS 2013, 27–29 Nov 2013. Revised selected papers, LNCS, vol. 8419, pp. 253–270. Springer, Berlin (2013)

  18. Zheng, Z., Webb, G.I.: Lazy learning of Bayesian rules. Mach. Learn. 41(1), 53–84 (2000)

    Article  Google Scholar 

  19. Keogh, E.J., Pazzani, M.J.: Learning augmented Bayesian classifiers: a comparison of distribution-based and classification-based approaches. In: Proceedings of the 7th International Workshop on AI and Statistics, pp 225–230 (1999)

  20. Webb, I.G., Boughton, R.J., Wang, Z.: Not so Naive Bayes: aggregating one-dependence estimators. Mach. Learn. 58(1), 5–24 (2005)

    Article  MATH  Google Scholar 

  21. Webb, G.I., Boughton, J.R., Zheng, F., Ting, K.M., Salem, H.: Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification. Mach. Learn. 86(2), 233–272 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  22. TELECOM ParisTech SEN research group: DPA Contest (2nd edn.) (2009–2010) http://www.DPAcontest.org/v2/

  23. TELECOM ParisTech SEN research group: DPA Contest. 4th edn., (2013–2014) http://www.DPAcontest.org/v4/

  24. Powers, D.M.W.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness and correlation. Int. J. Mach. Learn. Technol. 2(1), 37–63 (2011)

    Article  MathSciNet  Google Scholar 

  25. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

  26. Cestnik, B.: Estimating probabilities: a crucial task in machine learning. In: Proceendings of the European Conference on Artificial Intelligence. (1990)

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Authors and Affiliations

  1. ESAT/COSIC and imec, KU Leuven, Kasteelpark Arenberg 10, bus 2452, 3001, Leuven-Heverlee, Belgium

    Stjepan Picek

  2. CNRS, IRISA, Rennes, France

    Annelie Heuser

  3. Secure-IC S.A.S., Threat Analysis Business Line, 35 510, Cesson-Sèvignè, France

    Sylvain Guilley

Authors
  1. Stjepan Picek
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  2. Annelie Heuser
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  3. Sylvain Guilley
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Corresponding author

Correspondence to Annelie Heuser.

Additional information

This work has been supported in part by Croatian Science Foundation under the project IP-2014-09-4882. In addition, this work was supported in part by the Research Council KU Leuven (C16/15/058) and IOF project EDA-DSE (HB/13/020).

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Picek, S., Heuser, A. & Guilley, S. Template attack versus Bayes classifier. J Cryptogr Eng 7, 343–351 (2017). https://doi.org/10.1007/s13389-017-0172-7

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  • DOI: https://doi.org/10.1007/s13389-017-0172-7

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