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Intrusion Detection using Supervised Learning with Feature Set Reduction

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 33 - Number 6
Year of Publication: 2011
Authors:
Yogendra Kumar Jain
Upendra
10.5120/4025-5738

Yogendra Kumar Jain and Upendra. Article: Intrusion Detection using Supervised Learning with Feature Set Reduction. International Journal of Computer Applications 33(6):22-31, November 2011. Full text available. BibTeX

@article{key:article,
	author = {Yogendra Kumar Jain and Upendra},
	title = {Article: Intrusion Detection using Supervised Learning with Feature Set Reduction},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {33},
	number = {6},
	pages = {22-31},
	month = {November},
	note = {Full text available}
}

Abstract

Intrusion detection systems intend to recognize attacks with a low false positive rate and high detection rate. Many feature selection methods introduced to eliminate redundant and irrelevant features, because raw features may abbreviate accuracy or robustness of classification. In this paper we are proposing the information gain technique for the selection of the features. A feature with the highest information gain is the criteria for the selection of the features. We reduced the features of the data set than run the algorithm. Result show that drastically decreased in learning time of the algorithm without compromising the accuracy which is desirable for good IDS.We analyse two learning algorithms (NB and BayesNet) for the task of detecting intrusions and compare their relative performances. We comment on the suitability of the BayesNet algorithm for the intrusion detection task based on its high accuracy and high true positive rate. We finally state the usefulness of machine learning to the field of intrusion detection.

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