EMSCA-2023-Latest

本研究贡献了一个名为EMSCA-2023-Latest的新数据集，旨在支持深度学习模型在跨设备可移植性方面的电磁侧通道分析。该数据集由都柏林大学学院计算机科学学院创建，包含来自Dragon Board和Amazon Echo Show的电磁侧通道数据，总计480,000个样本。数据集的创建过程涉及使用HackRF One设备捕获电磁辐射，并采用I/Q采样方法处理数据。该数据集适用于网络安全和数字取证领域，旨在解决IoT设备电磁侧通道分析中的设备变异性和环境因素问题，提高分析的准确性和可靠性。

This study contributes a novel dataset named EMSCA-2023-Latest, which aims to support electromagnetic side-channel analysis for deep learning models regarding cross-device portability. This dataset was developed by the School of Computer Science, University College Dublin, and contains electromagnetic side-channel data collected from Dragon Board and Amazon Echo Show, totaling 480,000 samples. The dataset creation process involves using the HackRF One device to capture electromagnetic radiation and adopting the I/Q sampling method for data processing. This dataset is applicable to the fields of cybersecurity and digital forensics, and is designed to address the issues of device variability and environmental factors in electromagnetic side-channel analysis of IoT devices, thereby improving the accuracy and reliability of analysis.