论文标题
侧通道拆卸的配置和收集因子
Configuration and Collection Factors for Side-Channel Disassembly
论文作者
论文摘要
已经探索了无数用途，方法和渠道进行侧向通道分析。但是，特定的实施注意事项通常未发表。本文探讨了某些测试配置和收集参数，例如输入电压，分流电阻，采样率和微控制器时钟频率，以及它们对侧通道分析性能的影响。所考虑的分析用例是使用微控制器功率侧通道进行拆卸和分类。在实验中，使用ATMEGA328P微控制器和AVR指令集的子集作为测试设备（DUT）。时间序列卷积神经网络（CNN）用于评估时钟周期保真度的分类性能。我们得出的结论是，配置和收集参数对性能具有有意义的影响，尤其是在影响指令跟踪噪声比（SNR）的情况下。此外，侧通道拆卸需要的数据收集和分析远高于Nyquist率。我们还发现，具有1公里分流的7V输入电压和250-500 msa/s的样本率在我们的应用中提供了最佳性能，收益率降低，或者在某些情况下以较高级别的降级下降。
Myriad uses, methodologies, and channels have been explored for side-channel analysis. However, specific implementation considerations are often unpublished. This paper explores select test configuration and collection parameters, such as input voltage, shunt resistance, sample rate, and microcontroller clock frequency, along with their impact on side-channel analysis performance. The analysis use case considered is instruction disassembly and classification using the microcontroller power side-channel. An ATmega328P microcontroller and a subset of the AVR instruction set are used in the experiments as the Device Under Test (DUT). A time-series convolutional neural network (CNN) is used to evaluate classification performance at clock-cycle fidelity. We conclude that configuration and collection parameters have a meaningful impact on performance, especially where the instruction-trace's signal to noise ratio (SNR) is impacted. Additionally, data collection and analysis well above the Nyquist rate is required for side-channel disassembly. We also found that 7V input voltage with 1 kiloohm shunt and a sample rate of 250-500 MSa/s provided optimal performance in our application, with diminishing returns or in some cases degradation at higher levels.
