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2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery
Robust Observation Selection for Intrusion Detection
Tianjin, China
August 14-August 16
ISBN: 978-0-7695-3735-1
| ASCII Text | x | ||
| Xiang Cheng, Yuan Tian, Yong-Qin Cui, Jun-Na Zhang, "Robust Observation Selection for Intrusion Detection," Fuzzy Systems and Knowledge Discovery, Fourth International Conference on, vol. 1, pp. 269-272, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/FSKD.2009.451, author = {Xiang Cheng and Yuan Tian and Yong-Qin Cui and Jun-Na Zhang}, title = {Robust Observation Selection for Intrusion Detection}, journal ={Fuzzy Systems and Knowledge Discovery, Fourth International Conference on}, volume = {1}, year = {2009}, isbn = {978-0-7695-3735-1}, pages = {269-272}, doi = {http://doi.ieeecomputersociety.org/10.1109/FSKD.2009.451}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Fuzzy Systems and Knowledge Discovery, Fourth International Conference on TI - Robust Observation Selection for Intrusion Detection SN - 978-0-7695-3735-1 SP269 EP272 A1 - Xiang Cheng, A1 - Yuan Tian, A1 - Yong-Qin Cui, A1 - Jun-Na Zhang, PY - 2009 KW - SVM;feature selection; artificial intelligence VL - 1 JA - Fuzzy Systems and Knowledge Discovery, Fourth International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FSKD.2009.451
In many applications, one has to actively select among a set of expensive observations before making an informed decision. In this paper, we describe a hybrid of a simple artificial intelligence algorithm and a method based on class separability applied to the selection of feature subsets for classication problems. The method allows an expert to discover informative features for separation of normal and attack instances. Experiments performed on the KDD Cup dataset show that explanations provided by the method reveal the nature of attacks. Application of the method for feature selection yields a major improvement of detection accuracy.
Index Terms:
SVM;feature selection; artificial intelligence
Citation:
Xiang Cheng, Yuan Tian, Yong-Qin Cui, Jun-Na Zhang, "Robust Observation Selection for Intrusion Detection," fskd, vol. 1, pp.269-272, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
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