Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/36955
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Type: Conference paper
Title: Online training of SVMs for real-time intrusion detection
Author: Zhang, Z.
Shen, H.
Citation: 18th 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.568-573
Publisher: IEEE
Publisher Place: Online
Issue Date: 2004
ISBN: 0769520510
Conference Name: International Conference on Advanced Information Networking and Applications (18th : 2004 : Fukuoka, Japan)
Statement of
Responsibility: 
Zonghua Zhang, Hong Shen
Abstract: To 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.
Description: Copyright © 2004 IEEE
RMID: 0020065782
DOI: 10.1109/AINA.2004.1283970
Appears in Collections:Computer Science publications

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