Abnormal Traffic Detection Method of Computer Communication Network Based on Transfer Learning
The traditional network traffic detection method finishes attribute classification directly without cleaning network traffic data,its detection accuracy is low.Therefore,an abnormal traffic detection method of computer communication network based on transfer learning is proposed.Firstly,the isolated forest algorithm is used for data cleaning to remove irrelevant network traffic data information.Then,based on the preprocessed network traffic data,a deep analysis of its time series data is conducted to discover the occurrence patterns and characteristics of abnormal traffic,and determine the time range of abnormal occurrence.Finally,based on transfer learning,abnormal traffic detection in computer communication networks is implemented.The experimental results indicate that the abnormal traffic detection method of computer communication network based on transfer learning has higher detection accuracy and better application effect.
transfer learningcomputer communicationnetwork abnormalitydetection method
万方数据
维普
