Abstract
The usage of deep learning in profiled side-channel analysis requires a careful selection of neural network hyperparameters. In recent publications, different network architectures have been presented as efficient profiled methods against protected AES implementations. Indeed, completely different convolutional neural network models have presented similar performance against public side-channel traces databases. In this work, we analyze how weight initializers’ choice influences deep neural networks’ performance in the profiled side-channel analysis. Our results show that different weight initializers provide radically different behavior. We observe that even high-performing initializers can reach significantly different performance when conducting multiple training phases. Finally, we found that this hyperparameter is more dependent on the choice of dataset than other, commonly examined, hyperparameters. When evaluating the connections with other hyperparameters, the biggest connection is observed with activation functions.
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References
Benadjila, R., Prouff, E., Strullu, R., Cagli, E., Dumas, C.: Deep learning for side-channel analysis and introduction to ASCAD database. J. Cryptographic Eng. 10(2), 163–188 (2020). https://doi.org/10.1007/s13389-019-00220-8
Bhasin, S., Bruneau, N., Danger, J.-L., Guilley, S., Najm, Z.: Analysis and improvements of the DPA contest v4 implementation. In: Chakraborty, R.S., Matyas, V., Schaumont, P. (eds.) SPACE 2014. LNCS, vol. 8804, pp. 201–218. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12060-7_14
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
Chollet, F., et al.: Keras (2015). https://keras.io
Coron, J.S., Kizhvatov, I.: An efficient method for random delay generation in embedded software. Cryptology ePrint Archive, Report 2009/419 (2009). https://eprint.iacr.org/2009/419
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625–660 (2010)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: IEEE International Conference on Computer Vision (ICCV 2015) 1502, February 2015. https://doi.org/10.1109/ICCV.2015.123
Heuser, A., Zohner, M.: Intelligent machine homicide - breaking cryptographic devices using support vector machines. In: COSADE, pp. 249–264 (2012)
Keras: Layer weight initializers. https://keras.io/api/layers/initializers/
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. Cryptology ePrint Archive, Report 2018/1023 (2018). https://eprint.iacr.org/2018/1023
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, December 2014
Koturwar, S., Merchant, S.: Weight initialization of deep neural networks (DNNS) using data statistics. CoRR abs/1710.10570 (2017). http://arxiv.org/abs/1710.10570
Lerman, L., Bontempi, G., Markowitch, O.: Power analysis attack: An approach based on machine learning. Int. J. Appl. Cryptol. 3(2), 97–115 (2014). https://doi.org/10.1504/IJACT.2014.062722
Lerman, L., Poussier, R., Bontempi, G., Markowitch, O., Standaert, F.-X.: Template attacks vs. machine learning revisited (and the curse of dimensionality in side-channel analysis). In: Mangard, S., Poschmann, A.Y. (eds.) COSADE 2014. LNCS, vol. 9064, pp. 20–33. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21476-4_2
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
Mangard, S., Oswald, E., Popp, T.: Power Analysis Attacks: Revealing the Secrets of Smart Cards. Advances in Information Security. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-38162-6
Peng, A.Y., Sing Koh, Y., Riddle, P., Pfahringer, B.: Using supervised pretraining to improve generalization of neural networks on binary classification problems. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11051, pp. 410–425. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10925-7_25
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. Cryptographic Hardware Embed. Syst. 2019(1), 209–237 (2018). https://doi.org/10.13154/tches.v2019.i1.209-237, https://tches.iacr.org/index.php/TCHES/article/view/7339
Picek, S., Samiotis, I.P., Kim, J., Heuser, A., Bhasin, S., Legay, A.: On the performance of convolutional neural networks for side-channel analysis. In: Chattopadhyay, A., Rebeiro, C., Yarom, Y. (eds.) SPACE 2018. LNCS, vol. 11348, pp. 157–176. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05072-6_10
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). https://eprint.iacr.org/2018/053
Xavier Glorot, Y.B.: Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249–256 (2010)
Zaid, G., Bossuet, L., Habrard, A., Venelli, A.: Methodology for efficient CNN architectures in profiling attacks. IACR Trans. Cryptographic Hardware Embed. Syst. 2020(1), 1–36 (2019). https://doi.org/10.13154/tches.v2020.i1.1-36, https://tches.iacr.org/index.php/TCHES/article/view/8391
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Li, H., Krček, M., Perin, G. (2020). A Comparison of Weight Initializers in Deep Learning-Based Side-Channel Analysis. In: Zhou, J., et al. Applied Cryptography and Network Security Workshops. ACNS 2020. Lecture Notes in Computer Science(), vol 12418. Springer, Cham. https://doi.org/10.1007/978-3-030-61638-0_8
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