|Enhancing Classification Accuracy using Feature Subset Selection in Intrusion Detection System (IDS)|
|S.A. Margaret1 , S. Padmavathi2|
1 Dept. of Computer Science, Marudupandiyar College of Arts and Science, Thanjavur, India.
2 Dept. of Computer Science, Marudupandiyar College of Arts and Science, Thanjavur, India.
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Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-7 , Page no. 44-50, Jul-2017
Online published on Jul 30, 2017
Copyright © S.A. Margaret, S. Padmavathi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: S.A. Margaret, S. Padmavathi, “Enhancing Classification Accuracy using Feature Subset Selection in Intrusion Detection System (IDS)”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.7, pp.44-50, 2017.
MLA Style Citation: S.A. Margaret, S. Padmavathi "Enhancing Classification Accuracy using Feature Subset Selection in Intrusion Detection System (IDS)." International Journal of Computer Sciences and Engineering 5.7 (2017): 44-50.
APA Style Citation: S.A. Margaret, S. Padmavathi, (2017). Enhancing Classification Accuracy using Feature Subset Selection in Intrusion Detection System (IDS). International Journal of Computer Sciences and Engineering, 5(7), 44-50.
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|Intrusion detection system (IDS) look into field has developed immensely in the previous decade. Enhancing the detection rate of client to root (C2R) assault class is an open research issue. Current IDS utilizes all information elements to recognize intrusions. A portion of the elements might be excess to the detection procedure. The reason for this experimental examination is to distinguish the vital elements to enhance the detection rate and diminish the false detection rate. The researched highlight subset choice strategies enhance the general exactness, detection rate of C2R assault class and furthermore diminish the computational cost. The exact outcomes have demonstrated a recognizable change in detection rate of C2R assault class with include subset determination methods.|
|Key-Words / Index Term :|
|Feature subset selection; classification; preprocessing; Intrusion detection system|
 Feng Yang, K. Z. Mao, Gary Kee Khoon Lee, Wenyin Tang, “Emphasizing Minority Class in LDA for Feature Subset Selection on High-Dimensional Small-Sized Problems”, IEEE Transactions on Knowledge and Data Engineering, Vol.27, Issue.1, PP.88 – 101, 2015.
 Qinbao Song, Jingjie Ni, Guangtao Wang, “A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data”, IEEE Transactions on Knowledge and Data Engineering, Vol.25, Issue.1, PP.1 – 14, 2013.
 Yong Liu, Feng Tang, Zhiyong Zeng, “Feature Selection Based on Dependency Margin”, IEEE Transactions on Cybernetics, Vol.45, Issue.6, PP.1209 – 1221, 2015.
 D. Asir Antony Gnana Singh, S. Appavu Alias Balamurugan, E. Jebamalar Leavline, “An empirical study on dimensionality reduction and improvement of classification accuracy using feature subset selection and ranking”, (INCOSET), PP.102 – 108, 2012.
 Chieng-Yi Chang, “Dynamic Programming as Applied to Feature Subset Selection in a Pattern Recognition System”, IEEE Transactions on Systems, Man, and Cybernetics, Vol.SMC-3, Issue.2, PP.166 – 171, 1973.
 Surya S. Durbha, Roger L. King, Nicolas H. Younan, “Wrapper-Based Feature Subset Selection for Rapid Image Information Mining”, IEEE Geoscience and Remote Sensing Letters, Vol.7, Issue.1, PP.43 – 47, 2010.
 A. Dastanpour, S. Ibrahim, R. Mashinchi, "Effect of Genetic Algorithm on Artificial Neural Network for Intrusion Detection System", International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.10-18, 2016.
 L. Boroczky, L. Zhao, K. P. Lee, “Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD”, IEEE Transactions on Information Technology in Biomedicine, Vol.10, Issue.3, PP.504 – 511, 2006.
 J. Yang, V. Honavar, “Feature subset selection using a genetic algorithm”, IEEE Intelligent Systems and their Applications, Vol.13, Issue.2, PP.44 – 49, 1998.
 Kashif Javed, Haroon A. Babri, Mehreen Saeed, “Feature Selection Based on Class-Dependent Densities for High-Dimensional Binary Data”, IEEE Transactions on Knowledge and Data Engineering, Vol.24, Issue.3, PP.465 – 477, 2012.
 Yongxuan Zhu, Xin Shan, Jun Guo, “Modified genetic algorithm based feature subset selection in intrusion detection system”, ISCIT 2005, Vol.1, PP.10 – 13, 2005.
 Prachi Tembhare, Neeraj Shukla, "An Integrated and Improved Scheme for Efficient Intrusion Detection in Cloud", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.74-78, 2017.