Software Engineering Institute | Carnegie Mellon University
Software Engineering Institute | Carnegie Mellon University

Digital Library

Javascript is currently disabled for your browser. For an optimal search experience, please enable javascript.

Advanced Search

Basic Search

Content Type

Topics

Publication Date

White Paper

Identifying P2P Heavy-Hitters from Network-Flow Data

  • Abstract

    One major new and often not welcome source of Internet traffic is P2P filesharing traffic. Banning P2P
    usage is not always possible or enforceable, especially in a university environment. A more restrained approach allows P2P usage, but limits the available bandwidth. This approach fails when users start to use non-default ports for the client software. The PeerTracker algorithm, presented in this paper, allows detection of running P2P clients from NetFlow data in near real-time. The algorithm is especially suitable to identify clients that generate large amounts of traffic. A prototype system based on the PeerTracker algorithm is currently used by the network operations staff at the Swiss Federal Institute of Technology Zurich. We present measurements done on a medium sized Internet backbone and discuss accuracy issues, as well as possibilities and results from validation of the detection algorithm by direct polling in real-time.

  • Download

Part of a Collection

FloCon 2005 Collection