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.

中文翻译：

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用于 IIoT 入侵检测的微控制器单元芯片温度指纹通知机器学习


工业互联网的物理知识学习对于安全问题尤其重要。因此，人们开发了各种方法来进行工业物联网（IIoT）入侵检测。然而，传统方法通常需要辅助设备（例如频谱分析仪、对数周期天线）的帮助，由于其通用性较差，不适合一般的IIoT系统。面对上述困境，本文提出了一种基于微控制器单元（MCU）芯片温度指纹的机器学习方法，称为MTID，用于IIoT入侵检测。具体地，首先记录节点的MCU温度序列，并分析温度序列与节点计算复杂度之间的关系。然后，我们计算温度残差并构建温度残差数据集。最后，为了识别节点的安全状态，构建了基于自编码器的入侵检测模型。此外，为了确保模型在工业物联网系统多样化部署环境下的适用性，开发并应用了在线增量训练方法。最后，我们在测试MTID的性能时，使用Raspberry Pi 4B进行实验分析。结果表明，MTID用于入侵检测的准确率达到89%，这也证明了基于MCU温度的入侵检测方法的可行性。