论文标题
基于ML的EM侧渠道拆卸器的实用方法
A Practical Methodology for ML-Based EM Side Channel Disassemblers
论文作者
论文摘要
为具有有限接口功能的嵌入式设备提供安全保证是越来越重要的任务。尽管这些设备没有传统的接口，但它们仍然会产生与执行指令相关的无意电磁信号。通过使用我们的方法来收集这些痕迹，并利用随机的森林算法来开发机器学习模型，我们构建了基于EM侧渠道的指令级别拆卸器。拆卸器在Arduino Uno板上进行了测试，从设备中的一个位置捕获的十二个说明中的痕迹的痕迹获得了88.69％的指令识别；与以前类似工作中报道的75.6％（对于二十个说明）相比，这是一种改进。
Providing security guarantees for embedded devices with limited interface capabilities is an increasingly crucial task. Although these devices don't have traditional interfaces, they still generate unintentional electromagnetic signals that correlate with the instructions being executed. By collecting these traces using our methodology and leveraging a random forest algorithm to develop a machine learning model, we built an EM side channel based instruction level disassembler. The disassembler was tested on an Arduino UNO board, yielding an accuracy of 88.69% instruction recognition for traces from twelve instructions captured at a single location in the device; this is an improvement compared to the 75.6% (for twenty instructions) reported in previous similar work.
