+. Bruneau, S. Guilley, A. Heuser, D. Marion, and O. Rioul, Optimal side-channel attacks for multivariate leakages and multiple models, J. Cryptographic Engineering, vol.7, issue.4, pp.331-341, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02287606

N. Bruneau, S. Guilley, A. Heuser, O. Rioul, ;. Kaoshiung et al., Masks will fall off -higher-order optimal distinguishers, Advances in Cryptology -ASIACRYPT 2014 -20th International Conference on the Theory and Application of Cryptology and Information Security, vol.8874, pp.344-365, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02287072

. +-11]-lejla, B. Batina, E. Gierlichs, M. Prouff, F. Rivain et al., Mutual information analysis: a comprehensive study, J. Cryptology, vol.24, issue.2, pp.269-291, 2011.

J. M. Bhm-+-19]-olivier-bronchain, C. Hendrickx, A. Massart, F. Olshevsky, and . Standaert, Leakage certification revisited: Bounding model errors in side-channel security evaluations, Cryptology ePrint Archive, 2019.

L. Bottou, Stochastic gradient descent tricks, Neural Networks: Tricks of the Trade -Second Edition, vol.7700, pp.421-436, 2012.

, Cryptographic Hardware and Embedded Systems -CHES 2014 -16th International Workshop, vol.8731, 2014.

E. Cagli, C. Dumas, and E. Prouff, Convolutional neural networks with data augmentation against jitter-based countermeasures -profiling attacks without pre-processing, Cryptographic Hardware and Embedded Systems -CHES 2017 -19th International Conference, vol.10529, pp.45-68, 2017.

I. Jean-sébastien-coron and . Kizhvatov, An efficient method for random delay generation in embedded software, Cryptographic Hardware and Embedded Systems -CHES 2009, 11th International Workshop, vol.5747, pp.156-170, 2009.

H. Cramér, Mathematical methods of statistics, p.185436716, 1999.

S. Chari, J. R. Rao, and P. Rohatgi, Template attacks, Cryptographic Hardware and Embedded Systems -CHES 2002, 4th International Workshop, vol.2523, pp.13-28, 2002.

P. Chaudhari and S. Soatto, Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks, 6th International Conference on Learning Representations, 2018.

M. Thomas, J. A. Cover, and . Thomas, Elements of information theory, 2006.

S. Eloi-de-chérisey, O. Guilley, P. Rioul, and . Piantanida, Best information is most successful mutual information and success rate in sidechannel analysis, IACR Trans. Cryptogr. Hardw. Embed. Syst, vol.2019, issue.2, pp.49-79, 2019.

A. Duc, S. Faust, and F. Standaert, Making masking security proofs concrete -or how to evaluate the security of any leaking device, Advances in Cryptology -EUROCRYPT 2015 -34th Annual International Conference on the Theory and Applications of Cryptographic Techniques, vol.9056, pp.401-429, 2015.

I. J. Goodfellow, Y. Bengio, and A. C. Courville, Deep Learning. Adaptive computation and machine learning, 2016.

R. Gilmore, N. Hanley, and M. Neill, Neural network based attack on a masked implementation of AES, IEEE International Symposium on Hardware Oriented Security and Trust, pp.106-111, 2015.

M. Hardt, Generalization Theory and Deep Nets, An introduction

G. Hospodar, B. Gierlichs, E. D. Mulder, I. Verbauwhede, and J. Vandewalle, Machine learning in side-channel analysis: a first study, J. Cryptographic Engineering, vol.1, issue.4, pp.293-302, 2011.

A. Heuser, O. Rioul, and S. Guilley, Good is not good enough -deriving optimal distinguishers from communication theory, Batina and Robshaw, vol.14, pp.55-74
URL : https://hal.archives-ouvertes.fr/hal-02286943

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proceedings of the 32nd International Conference on Machine Learning, vol.37, pp.448-456, 2015.

P. Diederik, J. Kingma, and . Ba, Adam: A method for stochastic optimization, 3rd International Conference on Learning Representations, 2015.

R. Kleinberg, Y. Li, and Y. Yuan, An alternative view: When does SGD escape local minima, Proceedings of the 35th International Conference on Machine Learning, vol.80, pp.2703-2712, 2018.

. Kph-+-19]-jaehun, S. Kim, A. Picek, S. Heuser, A. Bhasin et al., Make some noise. unleashing the power of convolutional neural networks for profiled side-channel analysis, 2019.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks

. Weinberger, Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems, pp.1106-1114, 2012.

Y. Lecun, Y. Bengio, and G. E. Hinton, Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015.

L. Lerman, G. Bontempi, and O. Markowitch, Power analysis attack: an approach based on machine learning, IJACT, vol.3, issue.2, pp.97-115, 2014.

L. Lerman, G. Bontempi, and O. Markowitch, A machine learning approach against a masked AES -reaching the limit of side-channel attacks with a learning model, J. Cryptographic Engineering, vol.5, issue.2, pp.123-139, 2015.

R. Lpb-+-15]-liran-lerman, G. Poussier, O. Bontempi, F. Markowitch, and . Standaert, Template attacks vs. machine learning revisited (and the curse of dimensionality in side-channel analysis), Constructive Side-Channel Analysis and Secure Design -6th International Workshop, vol.9064, pp.20-33, 2015.

S. Mangard, Hardware countermeasures against DPA ? A statistical analysis of their effectiveness, Topics in Cryptology -CT-RSA 2004, The Cryptographers' Track at the RSA Conference, vol.2964, pp.222-235, 2004.

Z. Martinasek, P. Dzurenda, and L. Malina, Profiling power analysis attack based on MLP in DPA contest V4.2, 39th International Conference on Telecommunications and Signal Processing, pp.223-226, 2016.

S. Mangard, E. Oswald, and T. Popp, Power analysis attacks -revealing the secrets of smart cards, 2007.

H. Maghrebi, T. Portigliatti, and E. Prouff, Breaking cryptographic implementations using deep learning techniques, Security, Privacy, and Applied Cryptography Engineering -6th International Conference, vol.10076, pp.3-26, 2016.

Z. Martinasek and V. Zeman, Innovative method of the power analysis. Radioengineering, vol.22, pp.586-594, 2013.

M. A. Nielsen, Neural networks and deep learning, 2018.

O. Colin, Z. Flynn, and . Chen, Chipwhisperer: An open-source platform for hardware embedded security research, Constructive Side-Channel Analysis and Secure Design -5th International Workshop, vol.8622, pp.243-260, 2014.

. Springer, , 2014.

P. Petrushev, Approximation by ridge functions and neural networks, SIAM Journal on Mathematical Analysis, vol.30, issue.1, pp.155-189, 1998.

S. Picek, A. Heuser, A. Jovic, S. Bhasin, and F. Regazzoni, The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations, IACR Trans. Cryptogr. Hardw. Embed. Syst, vol.2019, issue.1, pp.209-237, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01935318

A. Pinkus, Approximation theory of the MLP model in neural networks, Acta Numerica, vol.8, pp.143-195, 1999.

E. Prouff and M. Rivain, Theoretical and practical aspects of mutual information-based side channel analysis, IJACT, vol.2, issue.2, pp.121-138, 2010.

E. Prouff and M. Rivain, Masking against side-channel attacks: A formal security proof, Advances in Cryptology -EUROCRYPT 2013, 32nd Annual International Conference on the Theory and Applications of Cryptographic Techniques, vol.7881, pp.142-159, 2013.

R. Psb-+-18]-emmanuel-prouff, R. Strullu, E. Benadjila, C. Cagli, and . Dumas, Study of deep learning techniques for side-channel analysis and introduction to ascad database, Cryptology ePrint Archive, 2018.

F. Mathieu-renauld, N. Standaert, D. Veyrat-charvillon, D. Kamel, and . Flandre, A formal study of power variability issues and side-channel attacks for nanoscale devices, Advances in Cryptology -EUROCRYPT 2011 -30th Annual International Conference on the Theory and Applications of Cryptographic Techniques, vol.6632, pp.109-128, 2011.

. Shk-+-14]-nitish, G. E. Srivastava, A. Hinton, I. Krizhevsky, R. Sutskever et al., Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

F. Standaert, T. Malkin, and M. Yung, A unified framework for the analysis of side-channel key recovery attacks, 28th Annual International Conference on the Theory and Applications of Cryptographic Techniques, vol.5479, pp.443-461, 2009.

S. Shalev, -. Shwartz, and S. Ben-david, Understanding Machine Learning: From Theory to Algorithms, 2014.

S. Santurkar, D. Tsipras, A. Ilyas, A. Madry, ;. Hanna et al., How does batch normalization help optimization?, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems, pp.2488-2498, 2018.

F. Standaert, N. Veyrat-charvillon, E. Oswald, B. Gierlichs, M. Medwed et al., The world is not enough: Another look on second-order DPA, Advances in Cryptology -ASIACRYPT 2010 -16th International Conference on the Theory and Application of Cryptology and Information Security, vol.6477, pp.112-129, 2010.

B. Timon, Non-profiled deep learning-based side-channel attacks with sensitivity analysis, IACR Trans. Cryptogr. Hardw. Embed. Syst, vol.2019, issue.2, pp.107-131, 2019.

V. Vapnik, The Nature of Statistical Learning Theory. Information Science and Statistics, 1995.

V. Vapnik, An overview of statistical learning theory, IEEE Trans. Neural Networks, vol.10, issue.5, pp.988-999, 1999.

S. Daan-van-der-valk and . Picek, Bias-variance decomposition in machine learning-based side-channel analysis, IACR Cryptology ePrint Archive, p.570, 2019.

N. Veyrat-charvillon, M. Medwed, S. Kerckhof, and F. Standaert, Shuffling against side-channel attacks: A comprehensive study with cautionary note, Advances in Cryptology -ASIACRYPT 2012 -18th International Conference on the Theory and Application of Cryptology and Information Security, vol.7658, pp.740-757, 2012.

F. Wegener, T. Moos, and A. Moradi, DL-LA: deep learning leakage assessment: A modern roadmap for SCA evaluations, IACR Cryptology ePrint Archive, p.505, 2019.

G. Zaid, L. Bossuet, A. Habrard, and A. Venelli, Methodology for efficient CNN architectures in profiling attacks, IACR Cryptology ePrint Archive, p.803, 2019.