Title :
Real-time inferring network traffic patterns
Author_Institution :
Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China
Abstract :
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 to determine 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 a variable 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 improved efficiency of our approach in comparison to previous work on clustering traffic patterns.
Keywords :
computer network management; computer network security; pattern clustering; telecommunication traffic; CERNET backbone link; DoS attack detection; network security; network traffic pattern; real-time automatic traffic pattern identification; traffic management; traffic pattern clustering; Binary trees; Clustering algorithms; IP networks; Radiation detectors; Real time systems; Sorting; Traffic control; Data Mining; Network Meas urement; Traff ic Pattern;
Conference_Titel :
Consumer Communications and Networking Conference (CCNC), 2011 IEEE
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-8789-9
DOI :
10.1109/CCNC.2011.5766512