IoT (Internet of Things) refers to the network of interconnected physical devices, vehicles, home appliances, and other items embedded with sensors, software, and connectivity, enabling them to collect and exchange data. IoT Forensics is collecting and analyzing digital evidence from IoT devices to investigate cybercrimes, security breaches, and other malicious activities that may have taken place on these connected devices. In particular, EM-SCA has become an essential tool for IoT forensics due to its ability to reveal confidential information about the internal workings of IoT devices without interfering these devices or wiretapping their networks. However, the accuracy and reliability of EM-SCA results can be limited by device variability, environmental factors, and data collection and processing methods. Besides, there is very few research on these limitations that affects significantly the accuracy of EM-SCA approaches for the crossed-IoT device portability as well as limited research on the possible solutions to address such challenge. Therefore, this empirical study examines the impact of device variability on the accuracy and reliability of EM-SCA approaches, in particular machine-learning (ML) based approaches for EM-SCA. We firstly presents the background, basic concepts and techniques used to evaluate the limitations of current EM-SCA approaches and datasets. Our study then addresses one of the most important limitation, which is caused by the multi-core architecture of the processors (SoC). We present an approach to collect the EM-SCA datasets and demonstrate the feasibility of using transfer learning to obtain more meaningful and reliable results from EM-SCA in IoT forensics of crossed-IoT devices. Our study moreover contributes a new dataset for using deep learning models in analysing Electromagnetic Side-Channel data with regards to the cross-device portability matter.

翻译：物联网（IoT）是指由嵌入传感器、软件和连接功能的物理设备、车辆、家用电器等互联网络构成的系统，这些设备能够收集和交换数据。物联网取证技术旨在收集和分析来自物联网设备的数字证据，以调查可能发生在这些联网设备上的网络犯罪、安全漏洞及其他恶意活动。特别地，电磁侧信道分析（EM-SCA）因其能够在不干扰设备或窃听其网络的情况下揭示物联网设备内部工作机密信息，已成为物联网取证的重要工具。然而，设备差异性、环境因素以及数据采集与处理方法会限制EM-SCA结果的准确性和可靠性。此外，关于这些显著影响跨物联网设备可移植性EM-SCA方法准确性的限制因素研究极为有限，且针对此类挑战的潜在解决方案的研究同样不足。为此，本实证研究考察了设备差异性对EM-SCA方法（尤其是基于机器学习的EM-SCA方法）准确性和可靠性的影响。我们首先介绍了评估当前EM-SCA方法及数据集局限性所需的背景、基本概念与技术。随后，本研究聚焦于由处理器多核架构（SoC）引发的最重要限制之一。我们提出了一种采集EM-SCA数据集的方法，并论证了利用迁移学习从跨物联网设备取证场景的EM-SCA中获得更具意义且更可靠结果的可行性。此外，本研究还贡献了一个用于深度学习模型分析电磁侧信道数据的新数据集，重点关注跨设备可移植性问题。