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EUNHYE KIM, SEUNGMIN LEE, KIHOON KWON+
AND SEHUN KIM++
Electronics and Telecommunications Research Institute
Daejeon, 305-700 Korea
+Samsung SDS
Seoul, 135-918 Korea
++Department of Industrial Engineering
Korea Advanced Institute of Science and Technology
Daejeon, 305-701 Korea
For computationally efficient and effective IDS, it is essential to identify important
input features. In this paper, a statistical feature construction scheme is proposed in which
factor analysis is orthogonally combined with an optimized k-means clustering technique.
As a core component for unsupervised anomaly detection, the proposed feature construction
scheme is able to exclude the redundancy of features optimally via the consideration
of the similarity of feature responses through a clustering analysis based on the
feature space reduced in a factor analysis. The performance of the proposed method was
evaluated using different data sets reduced by the ranking of the importance of input
features. Experimental results show a significant detection rate through a good subset of
features deemed to be critical to the improvement of the performance of classifiers.
Received March 27, 2008; revised October 28, 2008 & April 3, 2009; accepted May 7, 2009.
* This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information
Technology Research Center) support program supervised by the NIPA (National IT Industry
Promotion Agency) (NIPA-2009-(C1090-0902-0016)).