Abstract:Intelligent detection of network security vulnerabilities relies on a large amount of real data for analysis, and the presence of redundant and abnormal data can lead to a decrease in detection accuracy. In order to ensure the stable operation of the network system, the design and research of network security vulnerability intelligent detection system based on Knowledge graph is proposed. Design a network security vulnerability detector from three aspects: structure, logical model, and operation mode, to achieve hardware design of an intelligent network security vulnerability detection system; The system software design collects security vulnerability data through web crawlers, removes redundant data and abnormal data, identifies security vulnerability entities according to attribute information, and obtains security vulnerability attribute information relationships. Based on this, it defines the representation form of security vulnerability Knowledge graph, designs the structure of security vulnerability Knowledge graph, so as to realize the construction and visualization of security vulnerability Knowledge graph; Based on the above network design results, construct an overall architecture for intelligent detection of network security vulnerabilities, develop a specific process for intelligent detection of network security vulnerabilities, and obtain the final intelligent detection results of network security vulnerabilities. The experimental results show that under different experimental conditions, the minimum network security vulnerability detection rate of the designed system after application is 1.23%, the maximum F1 value for network security vulnerability detection is 9.50, and the minimum response time for network security vulnerability detection is 1 second, confirming that the designed system has better security vulnerability detection performance.
