E. Ronen, A. Shamir, A. Weingarten, and C. O'flynn, Iot goes nuclear: Creating a zigbee chain reaction, 2017 IEEE Symposium on Security and Privacy, pp.195-212, 2017.
DOI : 10.1109/msp.2018.1331033

S. Chari, J. R. Rao, and P. Rohatgi, Template Attacks, LNCS, vol.2523, pp.13-28, 2002.
DOI : 10.1007/3-540-36400-5_3

URL : https://link.springer.com/content/pdf/10.1007%2F3-540-36400-5_3.pdf

A. Heuser, O. Rioul, and S. Guilley, Good is Not Good Enough-Deriving Optimal Distinguishers from Communication Theory, Lecture Notes in Computer Science, vol.8731, 2014.
DOI : 10.1007/978-3-662-44709-3_4

L. Lerman, R. Poussier, G. Bontempi, O. Markowitch, and F. Standaert, Template attacks vs. machine learning revisited (and the curse of dimensionality in side-channel analysis), Constructive SideChannel Analysis and Secure Design-6th International Workshop, COSADE 2015, vol.9064, pp.20-33, 2015.

W. Schindler, K. Lemke, and C. Paar, A Stochastic Model for Differential Side Channel Cryptanalysis, LNCS, vol.3659, pp.30-46, 2005.
DOI : 10.1007/11545262_3

URL : https://link.springer.com/content/pdf/10.1007%2F11545262_3.pdf

O. Choudary and M. G. Kuhn, Efficient template attacks, Smart Card Research and Advanced Applications-12th International Conference, CARDIS 2013, vol.8419, pp.253-270, 2013.
DOI : 10.1007/978-3-319-14123-7_17

URL : https://www.repository.cam.ac.uk/bitstream/1810/245770/1/cardis2013-templates.pdf

T. M. Mitchell, Machine Learning. 1 edn, 1997.

A. Heuser and M. Zohner, Intelligent Machine Homicide-Breaking Cryptographic Devices Using Support Vector Machines, LNCS, vol.7275, pp.249-264, 2012.

G. Hospodar, B. Gierlichs, E. De-mulder, I. Verbauwhede, and J. Vandewalle, Machine learning in side-channel analysis: a first study, Journal of Cryptographic Engineering, vol.1, pp.293-302, 2011.

L. Lerman, G. Bontempi, and O. Markowitch, Power analysis attack: An approach based on machine learning, Int. J. Appl. Cryptol, 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.

L. Lerman, S. F. Medeiros, G. Bontempi, and O. Markowitch, A Machine Learning Approach Against a Masked AES, CARDIS. Lecture Notes in Computer Science, 2013.

S. Picek, A. Heuser, and S. Guilley, Template attack versus bayes classifier, Journal of Cryptographic Engineering, vol.7, issue.4, pp.343-351, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01629884

R. Gilmore, N. Hanley, and M. O'neill, Neural network based attack on a masked implementation of aes, 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp.106-111, 2015.

A. Heuser, S. Picek, S. Guilley, and N. Mentens, Lightweight ciphers and their sidechannel resilience, IEEE Transactions on Computers, issue.99, pp.1-1, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01629886

A. Heuser, S. Picek, S. Guilley, and N. Mentens, Side-channel analysis of lightweight ciphers: Does lightweight equal easy? In: Radio Frequency Identification and IoT Security-12th International Workshop, pp.91-104, 2016.

S. Picek, A. Heuser, A. Jovic, S. A. Ludwig, S. Guilley et al., Side-channel analysis and machine learning: A practical perspective, 2017 International Joint Conference on Neural Networks, pp.4095-4102, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01628681

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

E. Cagli, C. Dumas, and E. Prouff, Convolutional neural networks with data augmentation against jitter-based countermeasures-profiling attacks without preprocessing, Cryptographic Hardware and Embedded Systems-CHES 201719th International Conference, pp.45-68, 2017.

D. H. Wolpert, The Lack of a Priori Distinctions Between Learning Algorithms, Neural Comput, vol.8, issue.7, pp.1341-1390, 1996.

R. E. Bellman, Dynamic Programming, 2003.

G. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, vol.14, issue.1, pp.55-63, 1968.

K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks, vol.4, issue.2, pp.251-257, 1991.

N. Friedman, D. Geiger, and M. Goldszmidt, Bayesian Network Classifiers, Machine Learning, vol.29, issue.2, pp.131-163, 1997.

R. Collobert and S. Bengio, Links Between Perceptrons, MLPs and SVMs, Proceedings of the Twenty-first International Conference on Machine Learning. ICML '04, p.23, 2004.

J. H. Friedman, Greedy function approximation: A gradient boosting machine, Annals of Statistics, vol.29, pp.1189-1232, 2000.

T. Chen and C. Guestrin, Xgboost: A scalable tree boosting system, 2016.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.

Y. Lecun and Y. Bengio, Convolutional networks for images, speech, and time series, The handbook of brain theory and neural networks, vol.3361, 1995.

A. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals et al., Wavenet: A generative model for raw audio, 2016.

H. B. Demuth, M. H. Beale, O. De-jess, and M. T. Hagan, Neural network design, 2014.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., TensorFlow: Large-scale machine learning on heterogeneous systems (2015) Software available from tensorflow.org

F. Chollet, , 2015.

F. X. Standaert, T. Malkin, and M. Yung, A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks, LNCS, vol.5479, pp.443-461, 2009.

T. Paristech and S. Research, DPA Contest, 20092010.

T. Paristech and S. Research, DPA Contest, pp.2013-2014

J. Coron and I. Kizhvatov, An Efficient Method for Random Delay Generation in Embedded Software, Cryptographic Hardware and Embedded Systems-CHES 2009, 11th International Workshop, pp.156-170, 2009.

G. James, D. Witten, T. Hastie, and R. Tibsihrani, An Introduction to Statistical Learning. Springer Texts in Statistics, 2001.

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, Self-normalizing neural networks, 2017.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, Rectifier nonlinearities improve neural network acoustic models, in ICML Workshop on Deep Learning for Audio, Speech and Language Processing, 2013.

B. Timon, Non-profiled deep learning-based side-channel attacks, Cryptology ePrint Archive, 2018.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, 2015.