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Descripción
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| In this work we have presented a genetic algorithm approach for implementing rules deployed to detect various types of attacks. We have used the features identified as the most important ones for each attack type to form the rules in order to get a high detection rate. These features were identified by deploying principal component analysis and multi expression programming. Thenceforth we have used serial combination of the rules implemented for detection of user-to-root attacks with the rules implemented for detection of other types of attacks in order to decrease the false-positive rate. The model was verified on KDD99 intrusion detection dataset, demonstrating higher detection rates than those reported by the stateof-the-art, while using small subset of features from the original set. | |
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Internacional
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Si |
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Nombre congreso
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International Conference on Emerging Security Information, Systems and Technologies, Securware'07 |
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Tipo de participación
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960 |
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Lugar del congreso
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Valencia (España) |
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Revisores
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Si |
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ISBN o ISSN
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0-7695-2989-5 |
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DOI
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Fecha inicio congreso
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14/10/2007 |
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Fecha fin congreso
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20/10/2007 |
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Título de las actas
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