Abstract
Deep learning based side-channel analysis has seen a rise in popularity over the last few years. A lot of work is done to understand the inner workings of the neural networks used to perform the attacks and a lot is still left to do. However, finding a metric suitable for evaluating the capacity of the neural networks is an open problem that is discussed in many articles. We propose an answer to this problem by introducing an online evaluation metric dedicated to the context of side-channel analysis and use it to perform early stopping on existing convolutional neural networks found in the literature. This metric compares the performance of a network on the training set and on the validation set to detect underfitting and overfitting. Consequently, we improve the performance of the networks by finding their best training epoch and thus reduce the number of traces used by 30%. The training time is also reduced for most of the networks considered.
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
References
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
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). https://doi.org/10.1007/978-3-642-37288-9_18
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
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. http://dl.acm.org/citation.cfm?id=648255.752740
Goodfellow, I.J., Bengio, Y., Courville, A.C.: Deep Learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org/
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). https://doi.org/10.1007/s13389-011-0023-x
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). https://doi.org/10.13154/tches.v2019.i3.148-179
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_25
Lerman, L., Poussier, R., Markowitch, O., Standaert, F.: Template attacks versus machine learning revisited and the curse of dimensionality in side-channel analysis: extended version. J. Cryptogr. Eng. 8(4), 301–313 (2018). https://doi.org/10.1007/s13389-017-0162-9
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
Masure, L., Dumas, C., Prouff, E.: A comprehensive study of deep learning for side-channel analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2020(1), 348–375 (2019). https://doi.org/10.13154/tches.v2020.i1.348-375
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 (2018). https://doi.org/10.13154/tches.v2019.i1.209-237
Prouff, E., Strullu, R., Benadjila, R., Cagli, E., Dumas, C.: Study of deep learning techniques for side-channel analysis and introduction to ASCAD database. IACR Cryptology ePrint Archive 2018/53 (2018). http://eprint.iacr.org/2018/053
Standaert, F.-X., Malkin, T.G., Yung, M.: A unified framework for the analysis of side-channel key recovery attacks. In: Joux, A. (ed.) EUROCRYPT 2009. LNCS, vol. 5479, pp. 443–461. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01001-9_26
van der Valk, D., Picek, S.: Bias-variance decomposition in machine learning-based side-channel analysis. Cryptology ePrint Archive, Report 2019/570 (2019). https://eprint.iacr.org/2019/570
Zaid, G., Bossuet, L., Habrard, A., Venelli, A.: Methodology for efficient CNN architectures in profiling attacks. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2020(1), 1–36 (2019). https://doi.org/10.13154/tches.v2020.i1.1-36
Zhou, Y., Standaert, F.-X.: Deep learning mitigates but does not annihilate the need of aligned traces and a generalized ResNet model for side-channel attacks. J. Cryptogr. En. 10(1), 85–95 (2019). https://doi.org/10.1007/s13389-019-00209-3
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
Robissout, D., Zaid, G., Colombier, B., Bossuet, L., Habrard, A. (2021). Online Performance Evaluation of Deep Learning Networks for Profiled 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_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-68773-1_10
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)