Real-time inferring network traffic patterns

摘要

It is vitally important for applications in detecting DoS attacks, traffic management, and network security to real-time automatically identify traffic patterns in backbone networks with high speed links carrying large numbers of flows. Our objective is todet ermine traffic patterns that use up a disproportionate fraction of network resources. This paper first analyzes the major time and space cost in computing high volume clusters under different hierarchical structures, and then proposes avariable hierarchical structure to identify net work traffic patterns in a top-down fashion. We evaluate our model using real trace files from the CERNET backbone link an d demonstrate the imp roved efficiency of our approach in comparison to previous work on clustering traffic patterns.