International Journal of
Computer Sciences and Engineering

Scholarly Peer-Reviewed Scientific Research Publishing Journal
Enhancing Classification Accuracy using Feature Subset Selection in Intrusion Detection System (IDS)
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.
Correspondence should be addressed to: margaretjulie1970@gmail.com .

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-7 , Page no. 44-50, Jul-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i7.4450

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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Citation

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.
           
Abstract :
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
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