Research on Dimensionality Reduction in Network Traffic Anomaly Detection
摘要
To implement anomaly detection for a high dimensional network with mass flow data,data dimensionality should be reduced to relieve transmission and storage burdens from the system.This paper introduces network traffic anomaly detection process and dimensionality reduction ways in hig-speed network environment.Then it summarizes common features of feature in network traffic anomaly detection and latest research developments of dimensionality reduction for traffic data.Aiming at two kinds of feature dimensionality reduction ways,network traffic feature selection and network traffic feature extraction,this paper lists and classifies frequently used algorithms and describes the principles,advantages and disadvantages respectively.On this basis,this paper analyzes existing datasets and evaluation indexes used in research of dimensionality reduction.Finally,this paper discusses development directions and challenges of dimensionality reduction technologies in network traffic anomaly detection.