Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 38, Issue 6, Pages 7698-7707Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2010.12.141
Keywords
Network intrusion detection; Incremental support vector machine; Reserved set; Modified kernel function
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We develop an improved incremental SVM algorithm, named RS-ISVM, to deal with network intrusion detection. To reduce the noise generated by feature differences, we propose a modified kernel function U-RBF, with the mean and mean square difference values of feature attributes embedded in kernel function RBF. Then, given the oscillation problem that usually occurs in traditional incremental SVM's follow-up learning process, we present a reserved set strategy which can keep those samples that are more likely to be the support vectors in the following computation process. Moreover, in order to shorten the training time, a concentric circle method is suggested to be used in selecting samples to form the reserved set. Academic researches and data experiments show that RS-ISVM can ease the oscillation phenomenon in the learning process and achieve pretty good performance, meanwhile, its reliability is relative high. (C) 2010 Elsevier Ltd. All rights reserved.
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