Optimization of Network Security Intelligent Early Warning System Based on Image Matching Technology of Partial Differential Equation

Abstract

In order to effectively avoid network information leakage and computer network paralysis, and improve the ability of computer network security early warning, it is necessary to design a computer network security intelligent early warning system based on network behavior. Security warning is considered as the second defense mechanism behind the firewall. It can monitor and warn the network without affecting the network performance, so as to provide real-time protection for external attacks, internal attacks and misoperations, and improve the network security. This paper designs an intelligent early warning system for network security based on partial differential equation image matching technology. The system adopts B/S development mode, and the server and browser are located in the campus network. Data fusion technology is used to assess network security, predict potential threats, and add new intrusion features to the feature database for subsequent use. In this paper, a target matching algorithm based on partial differential equation is proposed by using partial differential equation. The algorithm calculates the phase difference through the algebraic combination of orthogonal filter outputs. For scenes with continuous disparity changes, the error matching rate of this algorithm is lower than that of Michael Bleyer algorithm and vertical constraint algorithm. In general, the disparity map generated by this algorithm has high matching accuracy. The results show that the distribution of alarm information is reasonable and in line with the actual situation.