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Discriminant Analysis based Feature Selection in KDD Intrusion Dataset

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011
Dr.S.Siva Sathya
Dr. R.Geetha Ramani

Dr.S.Siva Sathya, Dr. R.Geetha Ramani and K.Sivaselvi. Article:Discriminant Analysis based Feature Selection in KDD Intrusion Dataset. International Journal of Computer Applications 31(11):1-7, October 2011. Full text available. BibTeX

	author = {Dr.S.Siva Sathya and Dr. R.Geetha Ramani and K.Sivaselvi},
	title = {Article:Discriminant Analysis based Feature Selection in KDD Intrusion Dataset},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {31},
	number = {11},
	pages = {1-7},
	month = {October},
	note = {Full text available}


Intrusion detection system (IDS) plays a major role in providing network security by analyzing the network traffic log and classifying the records as attack or normal behavior. Generally, as each log record is characterized by a large set of features, an Intrusion Detection System consumes large computational power and time for the classification process. Hence, feature reduction becomes mandatory before attack classification for any IDS. Discriminant analysis is a technique which can be used for selecting important features in large set of features. In this paper, important features of KDD Cup ‘99 attack dataset are obtained using discriminant analysis method and used for classification of attacks. The results of discriminant analysis show that classification is done with minimum error rate with the reduced feature set.


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