Graph-based P2P traffic classification at the internet backbone

  • Authors:
  • Marios Iliofotou;Hyun-chul Kim;Michalis Faloutsos;Michael Mitzenmacher;Prashanth Pappu;George Varghese

  • Affiliations:
  • University of California, Riverside;CAIDA and Seoul National University;University of California, Riverside;Harvard University;Conviva, Inc.;University of California, San Diego

  • Venue:
  • INFOCOM'09 Proceedings of the 28th IEEE international conference on Computer Communications Workshops
  • Year:
  • 2009

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Abstract

Monitoring network traffic and classifying applications are essential functions for network administrators. In this paper, we consider the use of Traffic Dispersion Graphs (TDGs) to classify network traffic. Given a set of flows, a TDG is a graph with an edge between any two IP addresses that communicate; thus TDGs capture network-wide interactions. Using TDGs, we develop an application classification framework dubbed Graption (Graph-based classification). Our framework provides a systematic way to harness the power of network-wide behavior, flow-level characteristics, and data mining techniques. As a proof of concept, we instantiate our framework to detect P2P applications, and show that it can identify P2P traffic with recall and precision greater than 90% in backbone traces, which are particularly challenging for other methods.