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DC Field | Value | Language |
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dc.contributor.author | Alheeti, Khattab | - |
dc.contributor.author | Al-Jobouri, Laith | - |
dc.contributor.author | McDonald-Maier, Klaus | - |
dc.date.accessioned | 2022-10-23T20:12:07Z | - |
dc.date.available | 2022-10-23T20:12:07Z | - |
dc.date.issued | 2013-11-11 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/5647 | - |
dc.description.abstract | This 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.iso | en_US | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Intrusion detection | en_US |
dc.subject | Feature extraction | en_US |
dc.title | Increasing the rate of intrusion detection based on a hybrid technique | en_US |
dc.type | Article | en_US |
Appears in Collections: | قسم الشبكات |
Files in This Item:
File | Description | Size | Format | |
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Increasing the Rate of Intrusion Detection based on a Hybrid Technique.pdf | 220.7 kB | Adobe PDF | View/Open |
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