A fusion of ICA and SVM for detection computer attacks

Authors:
Surat Srinoy;Witcha Chimphlee;Siriporn Chimphlee;Yoothapoom Poopaibool
Affiliations:
Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, Thailand;Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, Thailand;Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, Thailand;Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, Thailand
Venue:
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
Year:
2006

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Abstract

Intrusion detection is the art of detecting unauthorized, inappropriate, or anomalous activity on computer systems. Independent component analysis (ICA) aims at extracting unknown hidden factors/components from multivariate data using only the assumption that unknown factors are mutually independent. In this paper it discuss an intrusion detection method that proposes independent component analysis based feature selection heuristics and using support vector machine for classification data. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset.