An Efficient Classifier approaches for Feature Reduction in Intrusion Detection

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-26 Number-1
Year of Publication : 2015
Authors : B.V. Ramnaresh Yadav, B. Satya Narayana, D. Vasumati
  10.14445/22312803/IJCTT-V26P107

MLA

B.V. Ramnaresh Yadav, B. Satya Narayana, D. Vasumati "An Efficient Classifier approaches for Feature Reduction in Intrusion Detection". International Journal of Computer Trends and Technology (IJCTT) V26(1):37-44, August 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Data security is primary concern in all service providing systems. Intrusion detection system is being popularly used for safeguard the data. But, traditional intrusion detection systems are based on derived knowledge of signature of known attacks which limit the scope of intrusion detection. The wide use of internet and its services in today life make high dependency over computer network and Web services systems. The dependency demands for a high network security for the exchange of confidential and secure information over the network communication channel. A secure information exchange can be made through deploying efficient intrusion detection for protection from various network attacks. Today most of the intrusion detection approaches focused on the issues of feature selection or reduction, since some of the features are irrelevant and redundant which results lengthy detection process and degrades the performance of an intrusion detection system (IDS). In this paper We analyze three feature reduction approaches to evaluate the accuracy of classification using NSL-KDD dataset.

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Keywords
IDS, FVBRM, GDA, FCDM, NSL-KDD dataset.