Objective:To explore the method of network abnormal traffic detection based on XGBoost algorithm and to evaluate its classification accuracy.Methods:Firstly,the XGBoost algorithm and the principle of principal component analysis were introduced,and the types,specific performance and causes of abnormal traffic were analyzed.Then,1364000 network traffic samples were used as the experimental data set,including 77 network traffic characteristics and 8 types of network traffic.Furthermore,the classification model of XGBoost was built to detect and identify the abnormal traffic effectively.Results:Experimental results showed that the detection accuracy of XGBoost algorithm for network abnormal traffic was 96.32%.Conclusion:The XGBoost algorithm had excellent performance and reliability in network abnormal traffic detection,and could provide effective assistant decision-making and protection measures for Network administrators.
维普
万方数据