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.

物联网取证中，设备变异性对机器学习方法的电磁侧信道分析（EM-SCA）的准确性和可靠性有重要影响，并且跨设备可移植性是一个需要解决的挑战。本研究通过实证研究探讨了设备变异性对 EM-SCA 方法的影响，并提出了使用迁移学习解决 EM-SCA 在物联网取证中的跨设备可移植性问题。