As side‐channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side‐channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning‐based side‐channel analysis. In particular, we outline how deep learning is applied to side‐channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

中文翻译：

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基于深度学习的辅助渠道分析的最新进展

由于边信道分析和机器学习算法具有相同的数据分类目标，因此提出了许多研究来使机器学习适应边信道分析。但是，机器学习算法的一个缺点是它们的性能取决于人类工程学。因此，该领域的最新研究集中于利用深度学习算法，该算法可以从数据中自动提取特征。在本研究中，我们调查了基于深度学习的副渠道分析的最新进展。特别是，我们概述了基于深度学习体系结构和应用方法的深度学习如何应用于边信道分析。此外，我们描述了使用不同体系结构和应用方法时的属性。最后，我们讨论了我们对该领域未来研究方向的看法。