Towards efficient intrusion detection systems based on machine learning techniques

Intrusion Detection System (IDS) have been the key in the network manager daily fight against continuous attacks. However, with the Internet growth, network security issues have become more difficult to handle. Jointly, Machine Learning (ML) techniques for traffic classification have been successful...

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Detalles Bibliográficos
Autores principales: Catania, Carlos, Vallés, Mariano, García Garino, Carlos
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 2010
Materias:
Ciencias Informáticas
sistema operativo
System architectures
Machine Learning (ML)
Intrusion Detection System (IDS)
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/19365
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-19365
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
sistema operativo
System architectures
Machine Learning (ML)
Intrusion Detection System (IDS)
spellingShingle Ciencias Informáticas
sistema operativo
System architectures
Machine Learning (ML)
Intrusion Detection System (IDS)
Catania, Carlos
Vallés, Mariano
García Garino, Carlos
Towards efficient intrusion detection systems based on machine learning techniques
topic_facet Ciencias Informáticas
sistema operativo
System architectures
Machine Learning (ML)
Intrusion Detection System (IDS)
description Intrusion Detection System (IDS) have been the key in the network manager daily fight against continuous attacks. However, with the Internet growth, network security issues have become more difficult to handle. Jointly, Machine Learning (ML) techniques for traffic classification have been successful in terms of performance classification. Unfortunately, most of these techniques are extremely CPU time consuming, making the whole approach unsuitable for real traffic situations. In this work, a description of a simple software architecture for ML based is presented together with the first steps towards improving algorithms efficience in some of the proposed modules. A set experiments on the 199 DARPA dataset are conducted in order to evaluate two atribute selecting algorithms considering not only classsification perfomance but also the required CPU time. Preliminary results show that computadtioal effort can be reduced by 50% maintaining similar accuaracy levels, progressing towards a real world implementation of an ML based IDS.
format Objeto de conferencia
Objeto de conferencia
author Catania, Carlos
Vallés, Mariano
García Garino, Carlos
author_facet Catania, Carlos
Vallés, Mariano
García Garino, Carlos
author_sort Catania, Carlos
title Towards efficient intrusion detection systems based on machine learning techniques
title_short Towards efficient intrusion detection systems based on machine learning techniques
title_full Towards efficient intrusion detection systems based on machine learning techniques
title_fullStr Towards efficient intrusion detection systems based on machine learning techniques
title_full_unstemmed Towards efficient intrusion detection systems based on machine learning techniques
title_sort towards efficient intrusion detection systems based on machine learning techniques
publishDate 2010
url http://sedici.unlp.edu.ar/handle/10915/19365
work_keys_str_mv AT cataniacarlos towardsefficientintrusiondetectionsystemsbasedonmachinelearningtechniques
AT vallesmariano towardsefficientintrusiondetectionsystemsbasedonmachinelearningtechniques
AT garciagarinocarlos towardsefficientintrusiondetectionsystemsbasedonmachinelearningtechniques
bdutipo_str Repositorios
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