Key Technologies For Edge Computing Information Security Protection Of Power Internet Of Things

The extensive use of edge computing in the power Internet of Things meets the storage and computing needs of massive data generated by power terminal equipment,but also brings a lot of information security risks,especially for data c lose to the edge.Leakage may occur in the process of analysis and analysis,ca using heavy losses to people’s production,life,and even national security.Therefore,power data on the edge side has become an important research object for information secur ity protection.The specific work of this paper is as follows:1.Introduce five key technologies for information security protection of power Internet of Things.A five-layer architecture in the edge computing environment of the power Internet of Things is proposed.Among them,the cloud layer is responsible for data storage and analysis,and dynamically allocates data;the edge layer is composed of nodes,controllers and edge gateways,which are the core of edge computing services and the transmission of data.Bridge;the terminal layer is a large number of sensing power devices;the remote network layer and the local network layer are responsible for meeting flexible and secure communication requirements.2.Propose an information security access control scheme based on user identity,access time and location attributes.Multiple authorized agencies manage attributes at the same time.Data users can only decrypt data that matches their own attribute policies.Security analysis and simulation experiments sh ow that this The scheme has less time overhead and storage overhead,and achieves safe and efficient fine-grained data access control.3.A data anomaly detection algorithm based on offset distance is proposed.The time series data collected by power equipment is divided into several sliding windows,and the Euclidean distance measurement method is used to calculate the offset distance within the window.When the offset distance of the data is greater than the threshold value Then it is determined that the local abnormality is in the window,and the number of local abnormality of the same data in different windows exceeds one third of the size of the sliding window,and the point is determined to be a global abnormal point.The window slides to the end of the time series data and stops,thereby obtaining a set of abnormal data points.The algorithm verification results show that the proposed algorithm has higher detection rate and lower false detection rate.