Big data network security system detection technology based on incremental support vector machine algorithm
Study the characteristics of using support vector machine algorithm to process large-scale network traffic data,and en-sure the classification accuracy of the model and the relationship between support vectors through association with incremental support vector machines.The study proposed and applied the incremental support vector machine algorithm to solve the anomaly detection problem in big data network security system detection.The algorithm comprehensively utilizes non-support vector samples and support vector samples in historical data to improve the performance and efficiency of the model.The research results show that the algorithm proposed in the study is significantly better than the traditional algorithm in terms of training time,but the classification accuracy is similar.In the end,the algorithm proposed in the study outperformed the traditional algorithm in terms of detection model performance after incremental learning.The detection time was 60.68％of the traditional algorithm,the anomaly detection rate increased by 12.41％,and the false alarm rate decreased by 52.94％.Therefore,the security system detection technology proposed in the study has important application prospects in the field of network security.
万方数据
维普
