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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Hybrid Intelligent Intrusion Detection Scheme
verfasst von : Mostafa A. Salama, Heba F. Eid, Rabie A. Ramadan, Ashraf Darwish, Aboul Ella Hassanien
Erschienen in: Soft Computing in Industrial Applications
Verlag: Springer Berlin Heidelberg
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This paper introduces a hybrid scheme that combines the advantages of deep belief network and support vector machine. An application of intrusion detection imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the intrusion into two outcomes: normal or attack, and the attacks fall into four classes; R2L, DoS, U2R, and Probing. First, we utilize deep belief network to reduct the dimensionality of the feature sets. This is followed by a support vector machine to classify the intrusion into five outcome; Normal, R2L, DoS, U2R, and Probing. To evaluate the performance of our approach, we present tests on NSL-KDD dataset and show that the overall accuracy offered by the employed approach is high.