Intrusion detection system based on support vector machine active learning and data fusion

Authors:
Man Zhao;Jing Zhai;Zhouqian He
Affiliations:
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China and School of Computer, China University of Geosciences, Wuhan, China;School of Computer, China University of Geosciences, Wuhan, China;School of Foreign Language, China University of Geosciences, Wuhan, China
Venue:
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Year:
2010

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Abstract

As the viruses and Trojans become more and more rampant and ingenious, the Intrusion Detection technology is a new security technology which is considered to be the second safe gate after the fire wall. This thesis brings forth new ideas of Intrusion Detection System based on support vector machine active learning and data fusion which is completely different from traditional IDSs. This IDS model has an improved algorithm in its incident analysor part that presents some advantages of finding details of concrete attack detecting efficiency and being convenient to update because of the dependence of each classifiers.