Network Security Defense Model and Simulation Analysis Based on Big Data

Abstract

The network has become an indispensable element in daily life and makes the connection between things closer. This paper presents a network security simulation model and discusses the basic framework and application of the model. This paper is based on the random forest network security situation awareness model. The model performs dimensionality reduction on the data through data feature analysis to highlight the characteristic attributes of data records and reduce invalid data, thereby reducing the resource overhead of the model and its dependence on network hardware device configuration. As the core of the model, the random forest algorithm enables the model to distinguish various network behaviors. Results show that the average detection rate of its network security model is 95%. Further reflects the high performance of this model.