Deep learning approaches have become popular for Side-Channel Analysis (SCA) in the recent years. Especially Convolutional Neural Networks (CNN) due to their natural ability to overcome jitter-based as well as masking countermeasures. Most of the recent works have been focusing on optimising the performance on given dataset, for example finding optimal architecture and using ensemble, and bypass the need for trace pre-processing. However, trace pre-processing is a long studied topic and several proven techniques exist in the literature. There is no straightforward manner to integrate those techniques into deep learning based SCA. In this paper, we propose a generic framework which allows seamless integration of multiple, user defined pre-processing techniques into the neural network architecture. The framework is based on Multi-scale Convolutional Neural Networks ( $\mathsf {MCNN}$ ) that were originally proposed for time series analysis. $\mathsf {MCNN}$ are composed of multiple branches that can apply independent transformation to input data in each branch to extract the relevant features and allowing a better generalization of the model. In terms of SCA, these transformations can be used for integration of pre-processing techniques, such as phase-only correlation, principal component analysis, alignment methods, etc . We present successful results on generic network which generalizes to different publicly available datasets. Our findings show that it is possible to design a network that can be used in a more general way to analyze side-channel leakage traces and perform well across datasets.

中文翻译：

---

返回基础知识：深度神经网络中边通道预处理的无缝集成

近年来，深度学习方法已广泛用于侧通道分析（SCA）。尤其是卷积神经网络（CNN），因为它们天生具有克服基于抖动以及掩盖对策的能力。最近的大多数工作都集中在优化给定数据集的性能上，例如寻找最佳架构和使用集成，并绕开了跟踪预处理的需要。但是，痕量预处理是一个长期研究的主题，并且文献中存在几种成熟的技术。没有直接的方式将这些技术集成到基于深度学习的SCA中。在本文中，我们提出了一个通用框架，该框架允许将多种用户定义的预处理技术无缝集成到神经网络体系结构中。 $ \ mathsf {MCNN} $ ），最初是为时间序列分析而提出的。 $ \ mathsf {MCNN} $ 由多个分支组成，这些分支可以对每个分支中的输入数据进行独立转换以提取相关特征，并可以更好地概括模型。就SCA而言，这些转换可用于集成预处理技术，例如仅相位相关，主成分分析，比对方法，等等 。我们在通用网络上展示了成功的结果，该网络推广到了不同的公开可用数据集。我们的发现表明，有可能设计一种可以以更通用的方式使用的网络来分析侧通道泄漏迹线并在整个数据集中表现良好。