A survey of feature selection in Internet traffic characterization

Mozo Velasco, Bonifacio Alberto ORCID: https://orcid.org/0000-0001-9743-8604 and Zhu, Bo (2014). A survey of feature selection in Internet traffic characterization. In: "First International Workshop on Big Data Applications and Principles", 11/09/2014 - 12/09/2014, Madrid, España. ISBN 978-84-15302-94-0. pp. 69-88.

Description

Title: A survey of feature selection in Internet traffic characterization
Author/s:
Item Type: Presentation at Congress or Conference (Lecture)
Event Title: First International Workshop on Big Data Applications and Principles
Event Dates: 11/09/2014 - 12/09/2014
Event Location: Madrid, España
Title of Book: First International Workshop on Big Data Applications and Principles : Proceedings
Date: September 2014
ISBN: 978-84-15302-94-0
Subjects:
Freetext Keywords: Feature selection, Internet traffic characterization, big data
Faculty: E.T.S.I. de Sistemas Informáticos (UPM)
Department: Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In the last decade, the research community has focused on new classification methods that rely on statistical characteristics of Internet traffic, instead of pre-viously popular port-number-based or payload-based methods, which are under even bigger constrictions. Some research works based on statistical characteristics generated large fea-ture sets of Internet traffic; however, nowadays it?s impossible to handle hun-dreds of features in big data scenarios, only leading to unacceptable processing time and misleading classification results due to redundant and correlative data. As a consequence, a feature selection procedure is essential in the process of Internet traffic characterization. In this paper a survey of feature selection methods is presented: feature selection frameworks are introduced, and differ-ent categories of methods are briefly explained and compared; several proposals on feature selection in Internet traffic characterization are shown; finally, future application of feature selection to a concrete project is proposed.

More information

Item ID: 35325
DC Identifier: https://oa.upm.es/35325/
OAI Identifier: oai:oa.upm.es:35325
Official URL: http://ict-ontic.eu/bigdap14/bigdap14_PROCEEDINGS....
Deposited by: Memoria Investigacion
Deposited on: 18 Mar 2016 20:23
Last Modified: 18 Mar 2016 20:23
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