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Multi-level Machine Learning Traffic Classification System

Authors:
Géza Szabó
János Szüle
Zoltán Turányi
Gergely Pongrácz

Keywords: traffic classification; machine learning; packet header

Abstract:
In this paper, we propose a novel framework for traffic classification that employs machine learning techniques and uses only packet header information. The framework consists of a number of key components. First, we use an efficient combination of clustering and classification algorithms to make the identification system robust in various network conditions. Second, we introduce traffic granularity levels and propagate information between the levels to increase accuracy and accelerate classification. Third, we use customized constraints based on connection patterns to efficiently utilize state-of-the-art clustering algorithms. The components of the framework are evaluated step-by-step to examine their contribution to the performance of the whole system.

Pages: 69 to 77

Copyright: Copyright (c) IARIA, 2012

Publication date: February 29, 2012

Published in: conference

ISSN: 2308-4413

ISBN: 978-1-61208-183-0

Location: Saint Gilles, Reunion

Dates: from February 29, 2012 to March 5, 2012