Dynamic Binary Tree for Hierarchical Clustering of IP Traffic

Author

Truong, Patrick ; Guillemin, Fabrice

Author_Institution

France Telecom R&D, Lannion

fYear

2007

fDate

26-30 Nov. 2007

Firstpage

6

Lastpage

10

Abstract

This paper proposes a computational and memory-efficient technique for online unidimensional clustering of individual IP addresses in order to detect high-volume traffic clusters (hierarchical heavy hitters). Our technique is based on a Patricia tree and can cope with today´s traffic volume. We test our algorithm by using a traffic trace composed of NetFlow records sent by a few tens of routers of the France telecom IP backbone network. We moreover show how our algorithm can be used for network anomaly detection.

Keywords

IP networks; Internet; computer network management; pattern clustering; telecommunication security; telecommunication traffic; tree data structures; HHH identification; IP network management; IP traffic hierarchical clustering; Internet; Patricia tree; dynamic binary tree; hierarchical heavy hitters; high-volume traffic cluster detection; individual IP addresses; memory-efficient technique; network anomaly detection; online unidimensional clustering; Binary trees; Clustering algorithms; Data security; Frequency; IP networks; Research and development; Spine; Telecommunication traffic; Testing; Volume measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Global Telecommunications Conference, 2007. GLOBECOM '07. IEEE

Conference_Location

Washington, DC

Print_ISBN

978-1-4244-1042-2

Electronic_ISBN

978-1-4244-1043-9

Type

conf

DOI

10.1109/GLOCOM.2007.9

Filename

4410919