Deep learning for side-channel analysis and introduction to ASCAD database
Deep learning for side-channel analysis and introduction to ASCAD database
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DOI：
10.1007/s13389-019-00220-8
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发表时间：
2019-11-30
影响因子：
1.9
通讯作者：
Dumas, Cecile
中科院分区：
文献类型：
--
作者：
Benadjila, Ryad;Prouff, Emmanuel;Dumas, Cecile
最近的研究表明，深度学习算法可以有效地对嵌入式系统进行安全评估，并且与其他方法相比具有许多优势。不幸的是，他们的超参数化经常被作者保密，他们只讨论了主要的设计原则和一些特定环境下的攻击效率。这显然是以前工作的一个重要限制，因为(1)后一种参数化被认为是机器学习中的一个具有挑战性的问题，(2)它不允许所呈现结果的可重复性，(3)它不允许得出一般结论。本文旨在从几个方面解决这些限制。首先，完成最近的工作，我们提出了深度学习算法在侧信道分析背景下的应用研究，并讨论了与经典模板攻击的联系。其次，我们首次解决了类卷积神经网络超参数的选择问题。在分析AES算法具有挑战性的掩码实现的背景下，给出了几个基准和基本原理。有趣的是，我们的工作表明，设计用于图像识别的算法VGG-16的方法在修复侧信道分析的架构时似乎也是合理的。为了实现我们测试的完美再现性，这项工作还引入了一个开放平台，包括目标实现的所有来源以及我们基准测试中利用的电磁测量活动。这个名为ASCAD的开放数据库是该类别中的第一个数据库，它已被指定为该主题进一步工作的共同基础。
Recent works have demonstrated that deep learning algorithms were efficient to conduct security evaluations of embedded systems and had many advantages compared to the other methods. Unfortunately, their hyper-parametrization has often been kept secret by the authors who only discussed on the main design principles and on the attack efficiencies in some specific contexts. This is clearly an important limitation of previous works since (1) the latter parametrization is known to be a challenging question in machine learning and (2) it does not allow for the reproducibility of the presented results and (3) it does not allow to draw general conclusions. This paper aims to address these limitations in several ways. First, completing recent works, we propose a study of deep learning algorithms when applied in the context of side-channel analysis and we discuss the links with the classical template attacks. Secondly, for the first time, we address the question of the choice of the hyper-parameters for the class convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a challenging masked implementation of the AES algorithm. Interestingly, our work shows that the approach followed to design the algorithm VGG-16 used for image recognition seems also to be sound when it comes to fix an architecture for side-channel analysis. To enable perfect reproducibility of our tests, this work also introduces an open platform including all the sources of the target implementation together with the campaign of electromagnetic measurements exploited in our benchmarks. This open database, named ASCAD, is the first one in its category and it has been specified to serve as a common basis for further works on this subject.