Microcontroller Unit Chip Temperature Fingerprint Informed Machine Learning for IIoT Intrusion Detection

Abstract

Physics-informed learning for industrial Internet is essential especially to safety issues. Consequently, various methods have been developed to conduct Industrial Internet of Things (IIoT) intrusion detection. However, the conventional methods usually require the help of auxiliary equipment (e.g., spectrum analyzers, log-periodic antennas), which proves to be unsuitable for general IIoT systems due to their poor versatility. Facing the dilemma mentioned above, this article proposes a microcontroller unit (MCU) chip temperature fingerprint informed machine learning method, called MTID, for IIoT intrusion detection. Specifically, first, the node's MCU temperature sequence is recorded and the relationship between the temperature sequence and the computational complexity of the node is analyzed. Then, we calculate the temperature residuals and construct a temperature residuals dataset. Finally, to identify the security status of the nodes, a self-encoder-based intrusion detection model is constructed. Furthermore, to ensure the model's applicability under the diversified deployment environment of IIoT systems, an online incremental training method is developed and applied. In the end, we use the Raspberry Pi 4B for experimental analysis when testing the performance of MTID. The results show that the accuracy of MTID for intrusion detection reaches 89%, which also demonstrates the feasibility of the intrusion detection method based on MCU temperature.