A Hybrid Feature Selection Method for Improved Detection of Wired/Wireless Network Intrusions

Internet has become an essential aspect of communication in the day to day life of everyone around the world. With the increased usage of Internet, attacks have also increased and the need for various levels of security is on the rise, both in wired and wireless environments. Intrusion detection sys...

Full description

Saved in:
Bibliographic Details
Published in:Wireless personal communications 2018, Vol.98 (2), p.1853-1869
Main Authors: Rene Beulah, J., Shalini Punithavathani, D.
Format: Article
Language:English
Subjects:
Accuracy
Communications Engineering
Computer Communication Networks
Cybersecurity
Engineering
Feature extraction
Intrusion detection systems
Networks
Signal,Image and Speech Processing
Wireless networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Internet has become an essential aspect of communication in the day to day life of everyone around the world. With the increased usage of Internet, attacks have also increased and the need for various levels of security is on the rise, both in wired and wireless environments. Intrusion detection system (IDS) has become a mandatory level of security for organizations to protect themselves from intruders. Improving the accuracy of IDS is crucial and it is the present focus of researchers. Feature selection has its role in enhancing accuracy by extracting the most relevant features. This study proposes a hybrid method for feature selection that picks and combines the best features from different feature selection methods. This method can be applied for feature reduction in any application domain. In this work, the proposed hybrid method is employed for intrusion detection and six predominant features are picked from NSL-KDD dataset. An exhaustive performance investigation has proved that the proposed feature selection method increases the detection rate by 5% thereby improving the accuracy of intrusion detection system by 3%.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-017-4949-x