Abstract
There is a revived interest in applying machine learning techniques for side-channel attacks, focusing on utilizing advancements in deep learning techniques. Most of the recent research work focuses on using a discriminative-learning-based classifier approach for profiled attacks, which we henceforth denote as a standard classifier approach. The standard classifier learns the intermediate target value in the training phase using a training loss function designed with classification accuracy. At the same time, the performance metric used for reporting results on a real attack dataset is generally key guessing entropy.
Although the standard classifiers are popular, they severely suffer from low classification accuracy (almost close to random guessing accuracy) on the attack and validation dataset. This also poses a problem in model selection with early stopping, and most of the literature does model selection at some arbitrary number of training epochs. This raises the concern that the standard classifier approach is ill-posed for the side-channel attack task, and it motivated us to investigate alternative ways of performing a side-channel attack.
This paper will introduce a novel multi-trunk binary classifier (MTOvC) approach as an alternative to a standard classifier. It exhibits good validation and attack dataset accuracies, suggesting that the resulting loss function is more suitable for the side-channel attack task. Moreover, good validation accuracies allow us to perform sensible model selection with early stopping in the case of multi-trunk classifiers.
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References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv:1603.04467 [cs] (2016). http://arxiv.org/abs/1603.04467
Benadjila, R., Prouff, E., Strullu, R., Cagli, E., Dumas, C.: Deep learning for side-channel analysis and introduction to ASCAD database. J. Cryptogr. Eng. 10(2), 163–188 (2019). https://doi.org/10.1007/s13389-019-00220-8
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics, Springer, New York (2006)
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
Choudary, O., Kuhn, M.G.: Efficient template attacks. IACR Cryptol. ePrint Arch, 770 (2013). http://eprint.iacr.org/2013/770
Gandolfi, K., Mourtel, C., Olivier, F.: Electromagnetic analysis: concrete results. In: Koç, Ç.K., Naccache, D., Paar, C. (eds.) CHES 2001. LNCS, vol. 2162, pp. 251–261. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44709-1_21
Genkin, D., Shamir, A., Tromer, E.: Acoustic cryptanalysis. J. Cryptol. 30(2), 392–443 (2016). https://doi.org/10.1007/s00145-015-9224-2
Goodfello, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Hospodar, G., Gierlichs, B., De Mulder, E., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptogr. Eng. 1(4), 293 (2011). https://doi.org/10.1007/s13389-011-0023-x
Kocher, Paul, Jaffe, Joshua, Jun, Benjamin: Differential power analysis. In: Wiener, Michael (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48405-1_25
Kocher, P.C.: Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. In: Koblitz, N. (ed.) CRYPTO 1996. LNCS, vol. 1109, pp. 104–113. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-68697-5_9
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw. 3361(10), 1995 (1995)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). http://www.nature.com/articles/nature14539
Lerman, L., Bontempi, G., Markowitch, O.: Power analysis attack: an approach based on machine learning. Int. J. Appl. Cryptograp. 3, ied Cryptography (2014)
Lu, X., Zhang, C., Cao, P., Gu, D., Lu, H.: Pay attention to raw traces: a deep learning architecture for end-to-end profiling attacks. IACR Trans. Cryptograph. Hardware Embed. Syst. 235–274 (2021). https://tches.iacr.org/index.php/TCHES/article/view/8974
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(86), 2579–2605 (2008). http://jmlr.org/papers/v9/vandermaaten08a.html
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
Martinasek, Z., Zeman, V.: Innovative method of the power analysis. Radioengineering 22(2), 9 (2013)
Memisevic, R.: An introduction to structured discriminative learning. Technical report, University of Toronto, Toronto, Canada, Technical report (2006)
Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001(December), pp. 3–8, 2001. Vancouver, British Columbia, Canada], pp. 841–848. MIT Press (2001). https://proceedings.neurips.cc/paper/2001/hash/7b7a53e239400a13bd6be6c91c4f6c4e-Abstract.html
Perin, G., Wu, L., Picek, S.: Exploring feature selection scenarios for deep learning-based side-channel analysis. Technical report 1414, na (2021). https://eprint.iacr.org/2021/1414
Picek, S., Heuser, A., Guilley, S.: Template attack vs bayes classifier. IACR Cryptol. ePrint Arch (2017)
Quisquater, Jean-Jacques., Samyde, David: Electromagnetic analysis (ema): measures and counter-measures for smart cards. In: Attali, Isabelle, Jensen, Thomas (eds.) E-smart 2001. LNCS, vol. 2140, pp. 200–210. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45418-7_17
Rijsdijk, J., Wu, L., Perin, G., Picek, S.: Reinforcement learning for hyperparameter tuning in deep learning-based side-channel analysis. IACR Trans. Cryptographic Hardware Embed. Syst. 677–707 (2021). https://tches.iacr.org/index.php/TCHES/article/view/8989
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
Wu, L., Perin, G., Picek, S.: I Choose you: automated hyperparameter tuning for deep learning-based side-channel analysis. IACR Cryptol. ePrint Arch, 1293 (2020). https://eprint.iacr.org/2020/1293
Zaid, G., Bossuet, L., Habrard, A., Venelli, A.: Methodology for efficient CNN architectures in profiling attacks. IACR Trans. Cryptographic Hardware Embed. Syst. 1–36 (2020). https://tches.iacr.org/index.php/TCHES/article/view/8391
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Kulkarni, P., Verneuil, V. (2022). Order vs. Chaos: Multi-trunk Classifier for Side-Channel Attack. In: Zhou, J., et al. Applied Cryptography and Network Security Workshops. ACNS 2022. Lecture Notes in Computer Science, vol 13285. Springer, Cham. https://doi.org/10.1007/978-3-031-16815-4_13
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