Anomaly detection method for power IoT terminals in zero-trust environment

With the digitalization of power grids and the development of new power systems, more and more IoT terminals are used in various scenarios of power grid business transmission and distribution, so as to realize full perception of system status and full service penetration in all links of electric energy production, transmission and consumption, and ensure the high-quality construction of power grids. The explosive growth of the number of IoT terminals (such as sensor devices, inspection robots, etc.) has transformed the system IT infrastructure from "boundary" to "borderless", and the network security boundary is gradually disintegrating, so the continuous anomaly detection security of power IoT terminals connected to the power grid system is becoming more and more important. To solve this problem, this paper proposes an anomaly detection method for power IoT terminals in a zero-trust environment. In this method, a BSREM credibility evaluation model based on behavioral sequence is designed to complete the anomaly detection task of IoT terminals, improve the information learning ability of the model by maintaining the information matrix, and expand the adaptation scope of the method. Finally, this paper uses simulation comparison to verify the BSREM model and LightLog model to evaluate the credibility of the device's traffic behavior and activity log. The results show that the BSREM model can complete the credibility evaluation task more efficiently.