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dc.contributor.authorZhang, Z.en
dc.contributor.authorShen, H.en
dc.identifier.citation18th International Conference on Advanced Information Networking and Applications : proceedings : AINA 2004, 29-31 March, 2004, Fukuoka, Japan / editor, Leonard Barolli, sponsored by IEEE Computer Society TCDP... [et al.] ; in cooperation with Fukuoka Institute of Technology (FIT) Japan ... [et al.], pp.568-573en
dc.descriptionCopyright © 2004 IEEEen
dc.description.abstractTo break the strong assumption that most of the training data for intrusion detectors are readily available with high quality, conventional SVM, Robust SVM and one-class SVM are modified respectively in virtue of the idea from Online Support Vector Machine (OSVM) in this paper, and their performances are compared with that of the original algorithms. Preliminary experiments with 1998 DARPA BSM data set indicate that the modified SVMs can be trained online and the results outperform the original ones with less support vectors(SVs) and training time without decreasing detection accuracy. Both of these achievements benefit an effective online intrusion detection system significantly.en
dc.description.statementofresponsibilityZonghua Zhang, Hong Shenen
dc.titleOnline training of SVMs for real-time intrusion detectionen
dc.typeConference paperen
dc.contributor.conferenceInternational Conference on Advanced Information Networking and Applications (18th : 2004 : Fukuoka, Japan)en
pubs.library.collectionComputer Science publicationsen
dc.identifier.orcidShen, H. [0000-0002-3663-6591]en
Appears in Collections:Computer Science publications

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