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7th IEEE International Conference on Computer and Information Technology (CIT 2007)
Comparison of Two Feature Selection Methods in Intrusion Detection Systems
Aizu-Wakamatsu City, Fukushima, Japan
October 16-October 19
ISBN: 0-7695-2983-6
ASCII Text
x 
 
M. J. Fadaeieslam, B. Minaei-Bidgoli, M. Fathy, M. Soryani, "Comparison of Two Feature Selection Methods in Intrusion Detection Systems," Computer and Information Technology, International Conference on, pp. 83-86, 7th IEEE International Conference on Computer and Information Technology (CIT 2007), 2007.
 
 
BibTex
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@article{ 10.1109/CIT.2007.99,
author = {M. J. Fadaeieslam and B. Minaei-Bidgoli and M. Fathy and M. Soryani},
title = {Comparison of Two Feature Selection Methods in Intrusion Detection Systems},
journal ={Computer and Information Technology, International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2983-6},
pages = {83-86},
doi = {http://doi.ieeecomputersociety.org/10.1109/CIT.2007.99},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer and Information Technology, International Conference on
TI - Comparison of Two Feature Selection Methods in Intrusion Detection Systems
SN - 0-7695-2983-6
SP83
EP86
A1 - M. J. Fadaeieslam,
A1 - B. Minaei-Bidgoli,
A1 - M. Fathy,
A1 - M. Soryani,
PY - 2007
VL - 0
JA - Computer and Information Technology, International Conference on
ER -
 
M. J. Fadaeieslam, Islamic Azad University - Semnan Branch
B. Minaei-Bidgoli, Iran University of Science and Technology
M. Fathy, Iran University of Science and Technology
M. Soryani, Iran University of Science and Technology
The quality of features directly affects the performance of classification. Many feature selection methods introduced to remove redundant and irrelevant features, because raw features may reduce accuracy or robustness of classification. In this paper we proposed a new method for feature selection based on Decision Dependent Correlation (DDC). We have used SVM classifier and the results on DARPA KDD99 benchmark dataset indicate that the proposed method outperforms Principal Component Analysis (PCA).
Citation:
M. J. Fadaeieslam, B. Minaei-Bidgoli, M. Fathy, M. Soryani, "Comparison of Two Feature Selection Methods in Intrusion Detection Systems," cit, pp.83-86, 7th IEEE International Conference on Computer and Information Technology (CIT 2007), 2007
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