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Network monitoring using traffic dispersion graphs (tdgs)
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Internet Measurement Conference archive
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement table of contents
San Diego, California, USA
SESSION: Measurements II table of contents
Pages: 315 - 320  
Year of Publication: 2007
ISBN:978-1-59593-908-1
Authors
Marios Iliofotou  University of California: Riverside, Riverside, CA
Prashanth Pappu  Rinera Networks, San Mateo, CA
Michalis Faloutsos  University of California: Riverside, Riverside, CA
Michael Mitzenmacher  Harvard University, Boston, MA
Sumeet Singh  Cisco Systems: Inc., San Jose, CA
George Varghese  University of California: San Diego, San Diego, CA
Sponsors
SIGCOMM: ACM Special Interest Group on Data Communication
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Monitoring network traffic and detecting unwanted applications has become a challenging problem, since many applications obfuscate their traffic using unregistered port numbers or payload encryption. Apart from some notable exceptions, most traffic monitoring tools use two types of approaches: (a) keeping traffic statistics such as packet sizes and interarrivals, flow counts, byte volumes, etc., or (b) analyzing packet content. In this paper, we propose the use of Traffic Dispersion Graphs (TDGs) as a way to monitor, analyze, and visualize network traffic. TDGs model the social behavior of hosts ("who talks to whom"), where the edges can be defined to represent different interactions (e.g. the exchange of a certain number or type of packets). With the introduction of TDGs, we are able to harness a wealth of tools and graph modeling techniques from a diverse set of disciplines.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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W. Aiello, C. Kalmanek, P. McDaniel, S. Sen, O. Spatscheck, and J. Merwe. Analysis of Communities of Interest in Data Networks. In Passive and Active Measurement Conference (PAM), 2005.
 
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D. Ellis, J. Aiken, A. McLeod, and D. Keppler. Graph-based Worm Detection on Operational Enterprise Networks. Technical Report MITRE Corporation, 2006.
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Collaborative Colleagues:
Marios Iliofotou: colleagues
Prashanth Pappu: colleagues
Michalis Faloutsos: colleagues
Michael Mitzenmacher: colleagues
Sumeet Singh: colleagues
George Varghese: colleagues