Compound Prediction Model of Information Network Security Situation Based on Support Vector Machine and Particle Swarm Optimization Algorithm

The composite prediction model for information network security situation based on support vector machine is an innovative method for predicting the security situation of information networks. This model combines multiple prediction models to obtain more accurate and reliable prediction results. The SVM algorithm is used as the basic model and then combined with other models such as decision trees, neural networks, and logistic regression. The composite prediction model considers various factors that affect the security status of information networks, including network traffic, system logs, and user behavior. By using machine learning algorithms to analyze these factors, the model can predict potential security threats before they occur. The use of SVM in this model provides several advantages over traditional methods. SVM has high accuracy and robustness when processing large datasets with complex features. It also has good generalization ability and can handle nonlinear relationships between variables. Overall, the support vector machine based composite prediction model for information network security situation is a promising method that can improve network security by providing warning signals for potential threats. The purpose of this study is to develop a new information network security situation prediction model. The proposed algorithm can be used for monitoring systems, which will help us predict the future evolution of security levels for each node at different stages. Our results indicate that our method can effectively predict the evolution trend of information network security situation based on support vector machines.