Abstract:
Internet of Things (IoT) 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 aim to investigate cybercrimes, security breaches, and other malicious activities that may have taken place on these connected devices. In particular, Electromagnetic Side-Channel Analysis (EM-SCA) has become an essential tool for IoT forensics due to its ability to reveal confidential information about the internal functions of IoT devices without interfering with 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. In fact, very few studies have explored potential solutions to address the significant impact of these limitations on the accuracy of EM-SCA approaches applied to crossed IoT devices. Therefore, this paper examines the impact of device variability on the accuracy and reliability of machine learning (ML)-based approaches for EM-SCA. We first present the background, basic concepts and techniques used to evaluate the limitations of current EM-SCA approaches and datasets. Our study then addresses one important limitation, which is caused by the multi-core architecture of the processors (aka. System-On-Chip). 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. Moreover, this study contributes a new dataset for using deep learning (DL) models in analyzing Electromagnetic Side-Channel data with regard to the cross-device portability matter
