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Feature Subset Selection in Intrusion Detection

 

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  • Product Description
 

Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. PCA has been employed to transform raw features into principal features space and select the features based on their sensitivity. This research applied a GA to search the principal feature space that offers a subset of features with optimal sensitivity. Based on the selected features, the classification is performed. The SVM and MLP are used for classification. This research work uses the KDD dataset. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Product Specifications
SKU :COC18447
AuthorIftikhar Ahmad
LanguageEnglish
BindingPaperback
Number of Pages220
Publishing Year1/18/2012
ISBN978-3847344964
Edition1 st
Book TypeComputer networking & communications
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-07-27 00:00:00
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