Abstract
Recent works in side-channel analysis have been fully relying on training classification models to recover sensitive information from traces. However, the knowledge of an attacker or an evaluator is not taken into account and poorly captured by solely training a classifier on signals. This paper proposes to inject prior information in preprocessing and classification in order to increase the performance of side-channel attacks (SCA). First we propose to use the Wavelet Scattering Transform, recently proposed by Mallat, for mapping traces into a time-frequency space which is stable under small translation and diffeomorphism. That way, we address the issues of desynchronization and deformation generally present in signals for SCA. The second part of our paper extends the canonical attacks over byte and Hamming weight by introducing a more general attack. Classifiers are trained on different labelings of the sensitive variable and combined by minimizing a cross-entropy criterion so as to find the best labeling strategy. With these two key ideas, we successfully increase the performance of Template Attacks on artificially desynchronized traces and signals from a jitter-protected implementation.
Univ. Grenoble Alpes, CNRS, Grenoble INP—Institute of Engineering Univ. Grenoble Alpes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
After preprocessing, wavelet scattering transform and spectrogram representations are vectorized before the PCA.
References
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). https://doi.org/10.1007/3-540-36400-5_3
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
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). https://doi.org/10.1007/978-3-319-49445-6_1
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). https://doi.org/10.1007/978-3-319-66787-4_3
Prouff, E., Strullu, R., Benadjila, R., Cagli, E., Dumas, C.: Study of deep learning techniques for side-channel analysis and introduction to ASCAD database. Cryptology ePrint Archive, Report 2018/053 (2018)
Yang, G., Li, H., Ming, J., Zhou, Y.: Convolutional neural network based side-channel attacks in time-frequency representations. In: Bilgin, B., Fischer, J.-B. (eds.) CARDIS 2018. LNCS, vol. 11389, pp. 1–17. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15462-2_1
Thiebeauld, H., Gagnerot, G., Wurcker, A., Clavier, C.: SCATTER: a new dimension in side-channel. In: Fan, J., Gierlichs, B. (eds.) COSADE 2018. LNCS, vol. 10815, pp. 135–152. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-89641-0_8
Agrawal, D., Archambeault, B., Rao, J.R., Rohatgi, P.: The EM side—channel(s). In: Kaliski, B.S., Koç, K., Paar, C. (eds.) CHES 2002. LNCS, vol. 2523, pp. 29–45. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36400-5_4
Plos, T., Hutter, M., Feldhofer, M.: Evaluation of side-channel preprocessing techniques on cryptographic-enabled HF and UHF RFID-tag prototypes. In: Workshop on RFID Security, pp. 114–127 (2008)
Gebotys, C.H., Ho, S., Tiu, C.C.: EM analysis of Rijndael and ECC on a wireless Java-based PDA. In: Rao, J.R., Sunar, B. (eds.) CHES 2005. LNCS, vol. 3659, pp. 250–264. Springer, Heidelberg (2005). https://doi.org/10.1007/11545262_19
Belgarric, P., et al.: Time-frequency analysis for second-order attacks. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 108–122. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08302-5_8
Debande, N., Souissi, Y., Aabid, M.A.E., Guilley, S., Danger, J.: Wavelet transform based pre-processing for side channel analysis. In: 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture Workshops, pp. 32–38 (2012)
Charvet, X., Pelletier, H.: Improving the DPA attack using wavelet transform. In: NIST Physical Security Testing Workshop, vol. 46 (2005)
van Woudenberg, J.G.J., Witteman, M.F., Bakker, B.: Improving differential power analysis by elastic alignment. In: Kiayias, A. (ed.) CT-RSA 2011. LNCS, vol. 6558, pp. 104–119. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19074-2_8
Muijrers, R.A., van Woudenberg, J.G.J., Batina, L.: RAM: rapid alignment method. In: Prouff, E. (ed.) CARDIS 2011. LNCS, vol. 7079, pp. 266–282. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-27257-8_17
Mallat, S.: Group invariant scattering. Commun. Pure Appl. Math. 65(10), 1331–1398 (2012)
Andén, J., Mallat, S.: Deep scattering spectrum. IEEE Trans. Signal Process. 62(16), 4114–4128 (2014)
Suzuki, D., Saeki, M., Ichikawa, T.: DPA leakage models for CMOS logic circuits. In: Rao, J.R., Sunar, B. (eds.) CHES 2005. LNCS, vol. 3659, pp. 366–382. Springer, Heidelberg (2005). https://doi.org/10.1007/11545262_27
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). https://doi.org/10.1007/11545262_3
Zhou, Z.-H.: Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC, Boca Raton (2012)
Massey, J.L.: Guessing and entropy. In: Proceedings of 1994 IEEE International Symposium on Information Theory, p. 204. IEEE (1994)
Gabor, D.: Theory of communication. Part 1: the analysis of information. J. Inst. Electr. Eng.-Part III Radio Commun. Eng. 93(26), 429–441 (1946)
Andreux, M., et al.: Kymatio: scattering transforms in Python. CoRR, abs/1812.11214 (2018)
Reddi, S.J., Kale, S., Kumar, S.: On the convergence of adam and beyond. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April–3 May 2018, Conference Track Proceedings (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Destouet, G., Dumas, C., Frassati, A., Perrier, V. (2021). Wavelet Scattering Transform and Ensemble Methods for Side-Channel Analysis. In: Bertoni, G.M., Regazzoni, F. (eds) Constructive Side-Channel Analysis and Secure Design. COSADE 2020. Lecture Notes in Computer Science(), vol 12244. Springer, Cham. https://doi.org/10.1007/978-3-030-68773-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-68773-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68772-4
Online ISBN: 978-3-030-68773-1
eBook Packages: Computer ScienceComputer Science (R0)