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首页> 外文期刊>IEEE transactions on very large scale integration (VLSI) systems >Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack
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Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

机译：面向基于深度学习的跨设备电源侧信道攻击的实用方法

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Power side-channel analysis (SCA) has been of immense interest to most embedded designers to evaluate the physical security of the system. This work presents profilingbased cross-device power SCA attacks using deep-learning techniques on 8-bit AVR microcontroller devices running AES-128. First, we show the practical issues that arise in these profiling-based cross-device attacks due to significant device-to-device variations. Second, we show that utilizing principal component analysis (PCA)-based preprocessing and multidevice training, a multilayer perceptron (MLP)-based 256-class classifier can achieve an average accuracy of 99.43% in recovering the first keybyte from all the 30 devices in our data set, even in the presence of significant interdevice variations. Results show that the designed MLP with PCA-based preprocessing outperforms a convolutional neural network (CNN) with four-device training by similar to 20% in terms of the average test accuracy of cross-device attack for the aligned traces captured using the ChipWhisperer hardware. Finally, to extend the practicality of these crossdevice attacks, another preprocessing step, namely, dynamic time warping (DTW) has been utilized to remove any misalignment among the traces, before performing PCA. DTW along with PCA followed by the 256-class MLP classifier provides >= 10.97% higher accuracy than the CNN-based approach for cross-device attack even in the presence of up to 50 time-sample misalignments between the traces.
机译：大多数嵌入式设计人员对于评估系统的物理安全性都非常关注电源侧信道分析（SCA）。这项工作使用运行于AES-128的8位AVR微控制器设备上的深度学习技术，提出了基于性能分析的跨设备电源SCA攻击。首先，我们展示由于设备之间的重大差异而在这些基于配置文件的跨设备攻击中出现的实际问题。其次，我们表明利用基于主成分分析（PCA）的预处理和多设备训练，基于多层感知器（MLP）的256类分类器可以从所有30个设备中恢复第一个密钥字节的平均精度达到99.43％。即使存在重大的设备间差异，我们的数据集也是如此。结果表明，针对使用ChipWhisperer硬件捕获的对齐迹线，跨设备攻击的平均测试准确度方面，所设计的基于PCA预处理的MLP优于具有四设备训练的卷积神经网络（CNN）。 。最后，为了扩展这些跨设备攻击的实用性，在执行PCA之前，还使用了另一个预处理步骤，即动态时间规整（DTW）来消除迹线之间的任何未对准情况。即使在迹线之间存在多达50个时间样本未对准的情况下，DTW以及PCA以及随后的256级MLP分类器也比基于CNN的跨设备攻击方法的准确性高出> 10.97％。

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