A Non-Intrusive Security Estimation Method based on Common Attribute of IIoT Systems

Kai Fang, Tingting Wang, Penglai Guo, Xiaoling Peng, Yuanyuan Pan, Xun Yuan, Jianqing Li

Abstract: Due to the limited computing power of Industrial Internet of Things (IIoT), it is impossible to port traditional attack resistance methods to run in IIoT systems. Currently, various methods have been developed to conduct security assessment for IIoT systems. However, these methods require modification of the original hardware of the IIoT system, so they are not universally applicable. In this paper, we propose a Non-intrusive Security Estimation Method (NSEM) for IIoT systems based on common attribute of IIoT devices. In the NSEM, we firstly record the common attribute (i.e. MCU chip temperatures) in different security states, and construct the temperature fingerprint dataset. Then, a Self-Encoder-based integration regression model is proposed to predict the security status of IIoT devices. Finally, we design a Cloud-Edge-End framework with model adaptive partitioning technology to support the efficient execution of the NSEM method. The experiments are conducted on Raspberry Pi 4B platforms. The results show that the Mean Squared Error (MSE) of the NSEM is only 0.001, and the Cloud-Edge-End framework can effectively improve execution efficiency.