Research on Campus Network Security Situational Awareness Technology Based on XGBoost Machine Learning

With the continuous popularisation of the Internet, mobile Internet and Internet of Things, the current campus network environment is constantly becoming more complex. As a result, the types and severity of campus network security threats are increasing, and the traditional means of campus network security protection has ushered in a huge challenge. Campus network security situational awareness is not only able to form timely monitoring of the security status changes of the current network environment, but also able to effectively predict the network development situation in the future period of time based on the collected network history data, so the campus network security situational awareness technology has great research value. This paper proposes an algorithm based on the parameters of the XGBoost neural network to achieve network security posture prediction to achieve an accurate prediction of the network security posture, the method completes the collection of historical data and statistics of the campus network risk, preprocesses the collected sample data, divides the training sample set from the established samples, and adopts the XGBOOST to train the sample set of the campus network security situational awareness. The campus network security posture trend of the network is predicted by the prediction model, and the results are used to guide the network security defence and early warning work, through the experimental analysis, the scheme has good posture prediction accuracy.