Evaluating Sequential Combination of Two Light-Weight Genetic Algorithm based Solutions to Intrusion Detection

Nieto-Taladriz García, Octavio; Bojanic, Slobodan y Bankovic, Zorana (2009). Evaluating Sequential Combination of Two Light-Weight Genetic Algorithm based Solutions to Intrusion Detection. En: "International Workshop on Computational Intelligence in Security for Information Systems, CISIS'08", 23/10/2008-24/10/2008, Génova, Italia. ISBN 1615-3871.

Descripción

Título: Evaluating Sequential Combination of Two Light-Weight Genetic Algorithm based Solutions to Intrusion Detection
Autor/es:
  • Nieto-Taladriz García, Octavio
  • Bojanic, Slobodan
  • Bankovic, Zorana
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: International Workshop on Computational Intelligence in Security for Information Systems, CISIS'08
Fechas del Evento: 23/10/2008-24/10/2008
Lugar del Evento: Génova, Italia
Título del Libro: Proceedings of International Workshop on Computational Intelligence in Security for Information Systems, CISIS'08
Fecha: 2009
ISBN: 1615-3871
Volumen: 59
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In this work we have presented a genetic algorithm approach for classifying normal connections and intrusions. We have created a serial combination of two light-weight genetic algorithm-based intrusion detection systems where each of the systems exhibits certain deficiency. In this way we have managed to mitigate the deficiencies of both of them. The model was verified on KDD99 intrusion detection dataset, generating a solution competitive with the solutions reported by the state-ofthe- art, while using small subset of features from the original set that contains forty one features. The most significant features were identified by deploying principal component analysis and multi expression programming. Furthermore, our system is adaptable since it permits retraining by using new data.

Más información

ID de Registro: 4321
Identificador DC: http://oa.upm.es/4321/
Identificador OAI: oai:oa.upm.es:4321
URL Oficial: http://www.springerlink.com/content/tr80677182q2u364/
Depositado por: Memoria Investigacion
Depositado el: 24 Sep 2010 10:00
Ultima Modificación: 20 Abr 2016 13:36
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