Network Security Education with Machine Learning Optimization

The current network security environment is increasingly severe, and more than static network protection mechanisms are needed to meet network security demands.Dynamic intrusion detection methods can provide real-time protection and detection for the network security environment.Therefore, this paper builds an intrusion detection education based on the machine learning algorithm and random forest and optimizes the model using the FSCA and TPE algorithms.Experimental results show that the intrusion detection model in this study has higher detection efficiency than other models and demonstrates strong adaptability, enabling rapid identification of intrusion behaviors and timely response in different intrusion detection environments, thereby improving the stability and practicality of the model.