Online training of SVMs for real-time intrusion detection

Zonghua Zhang, Hong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Advanced Information Networking and Applications, AINA 2004 Volume 1 (Regional Papers)
EditorsL. Barolli
Pages568-573
Number of pages6
Publication statusPublished - 2004
Externally publishedYes
EventProceedings - 18th International Conference on Advanced Information Networking and Applications, AINA 2004 - Fukuoka, Japan
Duration: 29 Mar 200431 Mar 2004

Publication series

NameProceedings - International Conference on Advanced Information Networking and Application (AINA)
Volume1

Conference

ConferenceProceedings - 18th International Conference on Advanced Information Networking and Applications, AINA 2004
Country/TerritoryJapan
CityFukuoka
Period29/03/0431/03/04

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