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Feature Construction Scheme for Efficient Intrusion Detection System
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- 발행일
- 201003
- 출처
- Journal of Information Science and Engineering, v.26 no.2, pp.527-547
- ISSN
- 1016-2364
- 출판사
- Academia Sinica
- 협약과제
- 10MC1100, 실시간 우편물류 운영기술 개발, 박종흥
- 초록
- 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 제안 키워드
- Clustering Analysis, Computationally Efficient, Construction Scheme, Data sets, Factor Analysis, Feature space, Input features, Intrusion detection system(IDS), K-means clustering technique, Statistical Features, detection rate(DR)