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Journal of Information Science and Engineering, Vol. 26 No. 2, pp. 527-547

Feature Construction Scheme for Efficient Intrusion Detection System

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.

Keywords: intrusion detection, feature construction, factor analysis, k-means clustering, self organizing map

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