NDLI: SVM ensemble intrusion detection model based on Rough Set feature reduct

Content Provider	IEEE Xplore Digital Library
Author	Zhang Hongmei
Copyright Year	2009
Description	Author affiliation: Information and communication College, Guilin University of Electronic Technology, 541004, China (Zhang Hongmei)
Abstract	To address the problem of low accuracy and high false alarm rate in network intrusion detection system, an Intrusion detection model of SVM ensemble using rough set feature reduct is presented. Utilizing the character that Rough set algorithm can keep the discernability of original dataset after reduction, the reducts of the original dataset are calculated and used to train individual SVM classifier for ensemble, which increase the diversity between individual classifiers, and consequently, increase the probability of detection accuracy improving. To validate the effectiveness of the proposed method, simulation experiments are performed based on the KDD 99 dataset. During the process of the experiments, two arguments, the sample number and the base classification number, are discussed to test their effect on the final result. And then detection performance comparison among the SVMusing all samples, SVM-Bagging ensemble and Rough Set based SVM-Bagging are performed. The results show that the Rough Set based SVM-Bagging is a promised ensemble method owning to its high diversity, high detection accuracy and faster speed in intrusion detection.
Starting Page	5604
Ending Page	5608
File Size	402286
Page Count	5
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5195196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probability Educational institutions Boosting Feature reduct Electronic mail Support vector machine Rough set Support vector machines Intrusion detection Support vector machine classification Machine learning Ensemble learning Bagging Testing
Content Type	Text
Resource Type	Article

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