The use of deep learning techniques to perform side-channel analysis attracted the attention of many researchers as they obtained good performances with them. Unfortunately, the understanding of the neural networks used to perform side-channel attacks is not very advanced yet. In this article, we propose to contribute to this direction by studying the impact of some particular deep learning techniques for tackling side-channel attack problems. More precisely, we propose to focus on three existing techniques: batch normalization, dropout, and weight decay, not yet used in side-channel context. By combining adequately these techniques for our problem, we show that it is possible to improve the attack performance, i.e., the number of traces needed to recover the secret, by more than 55%. Additionally, they allow us to have a gain of more than 34% in terms of training time. We also show that an architecture trained with such techniques is able to perform attacks efficiently even in the context of desynchronized traces.

中文翻译：

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使用性能改进技术改进深度学习网络以进行侧信道分析

使用深度学习技术进行边信道分析引起了许多研究人员的注意，因为他们获得了良好的性能。不幸的是，对用于执行侧信道攻击的神经网络的理解还不是很先进。在本文中，我们建议通过研究一些特定的深度学习技术对解决侧信道攻击问题的影响来为这个方向做出贡献。更准确地说，我们建议关注三种现有技术：批量归一化、dropout 和权重衰减，这些技术尚未用于边信道上下文。通过充分结合这些技术来解决我们的问题，我们表明可以将攻击性能（即恢复秘密所需的跟踪数量）提高 55% 以上。此外，它们使我们在训练时间方面获得了超过 34% 的收益。我们还表明，使用这种技术训练的架构即使在不同步跟踪的情况下也能够有效地执行攻击。