BROWSE
Detail
Journal Article
Feature Construction Scheme for Efficient Intrusion Detection System
Cited 4 time in 
Share 
Share
- Authors
- Eunhye Kim, Seungmin Lee, Kihoon Kwon, Sehun Kim
- Issue Date
- 2010-03
- Citation
- Journal of Information Science and Engineering, v.26, no.2, pp.527-547
- ISSN
- 1016-2364
- Publisher
- Academia Sinica
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Clustering Analysis, Computationally Efficient, Construction Scheme, Data sets, Detection Systems(IDS), Feature space, Input features, K-means clustering technique, Statistical Features, detection rate(DR), factor analysis