Intelligent Network Security Situation Prediction Method Based on Deep Reinforcement Learning

Abstract

The prediction of network security situations is the most basic link in the process of intelligent learning, which supports the further development of artificial intelligence technology and the development of new fields. With the help of deep reinforcement learning technology, the prediction effect of network desktops is further improved, which also guarantees the accuracy of the final prediction results. Therefore, an intelligent network security situation prediction method based on deep reinforcement learning is proposed. Calculate the network interference efficiency factor, create the differentiated time series prediction structure, construct the normalized deep reinforcement learning prediction model, expand the security situation prediction evolution under deep reinforcement learning, and use the hybrid optimization method to predict the network security situation. The experimental results show that compared with the traditional time-domain analysis test group, the prediction error of the deep reinforcement learning prediction group is relatively small, indicating that the prediction effect is better and has potential application significance.