| Abstract |
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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.
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 Additional Information
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Citation:
Zonghua Zhang, Hong Shen,
"Online Training of SVMs for Real-time Intrusion Detection,"
aina,
p. 568,
18th International Conference on Advanced Information Networking and Applications (AINA'04) Volume 1,
2004
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