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Non Gaussian and Long Memory Statistical Modeling of Internet Traffic

Abstract : Due to the variety of services and applications available on today's Internet, many properties of the traffic stray from the classical characteristics (Gaussianity and short memory) of standard models. The goal of the present contribution is to propose a statistical model able to account both for the non Gaussian and long memory properties of aggre- gated count processes. First, we introduce the model and a procedure to estimate the corresponding parameters. Second, using a large set of data taken from public reference repositories (Bellcore, LBL, Auckland, UNC, CAIDA) and collected by ourselves, we show that this stochastic process is relevant for Internet traffic modeling for a wide range of aggregation levels. In conclusion we indicate how this modeling could be used in IDS design.
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Submitted on : Friday, September 28, 2007 - 10:13:00 AM
Last modification on : Thursday, September 29, 2022 - 2:58:07 PM
Long-term archiving on: : Friday, April 9, 2010 - 3:03:30 AM


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  • HAL Id : ensl-00175418, version 1


Antoine Scherrer, Nicolas Larrieu, Pierre Borgnat, Philippe Owezarski, Patrice Abry. Non Gaussian and Long Memory Statistical Modeling of Internet Traffic. 4th International Workshop on Internet Performance, Simulation, Monitoring and Measurement (IPS-MoMe) 2006, Salzburg Research, Salzburg University, IST MoMe cluster, ACM SIGSIM, IEEE Austria, Feb 2006, Salzburg, Austria. pp.176-185. ⟨ensl-00175418⟩



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