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

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

Description

Title: Evaluating Sequential Combination of Two Light-Weight Genetic Algorithm based Solutions to Intrusion Detection
Author/s:
  • Nieto-Taladriz García, Octavio
  • Bojanic, Slobodan
  • Bankovic, Zorana
Item Type: Presentation at Congress or Conference (Article)
Event Title: International Workshop on Computational Intelligence in Security for Information Systems, CISIS'08
Event Dates: 23/10/2008-24/10/2008
Event Location: Génova, Italia
Title of Book: Proceedings of International Workshop on Computational Intelligence in Security for Information Systems, CISIS'08
Date: 2009
ISBN: 1615-3871
Volume: 59
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 4321
DC Identifier: http://oa.upm.es/4321/
OAI Identifier: oai:oa.upm.es:4321
Official URL: http://www.springerlink.com/content/tr80677182q2u364/
Deposited by: Memoria Investigacion
Deposited on: 24 Sep 2010 10:00
Last Modified: 20 Apr 2016 13:36
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