Feature Construction Scheme for Efficient Intrusion Detection System

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 320
  • Download : 0
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.
Publisher
INST INFORMATION SCIENCE
Issue Date
2010-03
Language
English
Article Type
Article
Citation

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, v.26, pp.527 - 547

ISSN
1016-2364
URI
http://hdl.handle.net/10203/98833
Appears in Collection
IE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0