## CryptoDB

### Paper: Make Some Noise. Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis

Authors: Jaehun Kim , Delft University of Technology, Delft Stjepan Picek , Delft University of Technology, Delft Annelie Heuser , Univ Rennes, Inria, CNRS, IRISA Shivam Bhasin , Physical Analysis and Cryptographic Engineering, Temasek Laboratories at Nanyang Technological University Alan Hanjalic , Delft University of Technology, Delft DOI: 10.13154/tches.v2019.i3.148-179 URL: https://tches.iacr.org/index.php/TCHES/article/view/8292 Search ePrint Search Google Slides Profiled side-channel analysis based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing significant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing a new Convolutional Neural Network instance able to reach high performance for a number of considered datasets. We compare our neural network with the one designed for a particular dataset with masking countermeasure and we show that both are good designs but also that neither can be considered as a superior to the other one.Next, we address how the addition of artificial noise to the input signal can be actually beneficial to the performance of the neural network. Such noise addition is equivalent to the regularization term in the objective function. By using this technique, we are able to reduce the number of measurements needed to reveal the secret key by orders of magnitude for both neural networks. Our new convolutional neural network instance with added noise is able to break the implementation protected with the random delay countermeasure by using only 3 traces in the attack phase. To further strengthen our experimental results, we investigate the performance with a varying number of training samples, noise levels, and epochs. Our findings show that adding noise is beneficial throughout all training set sizes and epochs.
##### BibTeX
@article{tches-2019-29322,
title={Make Some Noise. Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis},
journal={IACR Transactions on Cryptographic Hardware and Embedded Systems},
publisher={Ruhr-Universität Bochum},
volume={2019, Issue 3},
pages={148-179},
url={https://tches.iacr.org/index.php/TCHES/article/view/8292},
doi={10.13154/tches.v2019.i3.148-179},
author={Jaehun Kim and Stjepan Picek and Annelie Heuser and Shivam Bhasin and Alan Hanjalic},
year=2019
}