Deep Learning-Based Network Security Data Sampling and Anomaly Prediction in Future Network

Abstract

Based on the design idea of future network, this paper analyzes the network security data sampling and anomaly prediction in future network. Through game theory, it is determined that data sampling is performed on some important nodes in the future network. Deep learning methods are used on the selected nodes to collect data and analyze the characteristics of the network data. Then, through offline and real-time analyses, network security abnormal events are predicted in the future network. With the comparison of various algorithms and the adjustment of hyperparameters, the data characteristics and classification algorithms corresponding to different network security attacks are found. We have carried out experiments on the public dataset, and the experiment proves the effectiveness of the method. It can provide reference for the management strategy of the switch node or the host node by the future network controller.