基于多层Sketch的SDN网络流量测量技术研究OA北大核心CSTPCD

Network traffic measurement for large flow detection,mutation flow detection and base estimation is of great significance for ensuring network security.However,the current related research suffers from the problems of insufficient real-time performance and low measurement accuracy.In response to the above issues,this paper designs a network traffic measurement model based on Multiple Layer Sketch(ML Sketch).First,the model adopts an independently designed ML Sketch structure,which uses a categorized storage structure to improve the accuracy of traffic measurement.Second,we simulate the dynamic occurrence scenarios of traffic in SDN(Software Defined Network)environment using real-time traffic playback technology.Finally,real-time dynamic detection of large,mutating and base estimation classes of traffic is realized in the SDN control plane.The experimental results on UNSW-NB15 show that compared with the traditional Sketch structure,the ML Sketch structure designed in this paper improves the F1_Score metric by up to 4.81％and reduces the correlation error by up to 81.12％,verifying the effectiveness of the model in this paper.