Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/5647
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dc.contributor.authorAlheeti, Khattab-
dc.contributor.authorAl-Jobouri, Laith-
dc.contributor.authorMcDonald-Maier, Klaus-
dc.date.accessioned2022-10-23T20:12:07Z-
dc.date.available2022-10-23T20:12:07Z-
dc.date.issued2013-11-11-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/5647-
dc.description.abstractThis paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it's shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.subjectIntrusion detectionen_US
dc.subjectFeature extractionen_US
dc.titleIncreasing the rate of intrusion detection based on a hybrid techniqueen_US
dc.typeArticleen_US
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