Research on Cybersecurity Threat Prediction and Prevention Technology for Intelligent Housing Combined with Edge Computing

Abstract

As an important application scenario for the development of Internet of Things (IoT) technology, smart housing is facing unprecedented challenges in its network security. The traditional centralized security protection architecture in the face of large-scale distributed smart devices have problems such as high latency, slow response, etc., and it is difficult to meet the real-time threat detection and protection needs. Aiming at the problems of insufficient real-time threat detection and limited protection effect of smart housing network security, this paper proposes a smart housing network security threat prediction and prevention technology combined with edge computing. First of all, the distributed security monitoring architecture based on edge computing nodes is constructed, the K-means clustering algorithm is used for network traffic anomaly detection, the support vector machine is used to achieve threat intrusion identification, and the dynamic heterogeneous redundancy adaptive defense mechanism is established; then, the multilayer threat detection model is designed, and the behavioral anomaly analysis and threat prediction are combined with machine learning algorithms; finally, the edge infrastructure is secured and trusted through the edge, data security and network security three-layer protection system to build a complete security prevention framework. The experimental results show that the data processing rate of the system reaches 2350 items/s, the mean value of threat detection error is 0.396, the monthly average defense rate reaches 96.262%, and the path interception probability is as low as 0.935.The research results validate the effectiveness of the edge computing technology in the prediction and prevention of network security threats in smart housing, and provide new technical paths for the construction of efficient real-time security protection system of smart housing. The results verify the effectiveness of edge computing technology in the prediction and prevention of smart housing network security threats, and provide a new technical path for building an efficient real-time smart housing security protection system.