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Journal of Electrical and Computer Engineering
Volume 2017 (2017), Article ID 1794849, 6 pages
Research Article

Security Enrichment in Intrusion Detection System Using Classifier Ensemble

1Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, India
2Sinhgad Institute of Technology and Science, Savitribai Phule Pune University, Narhe, Pune, India

Correspondence should be addressed to Uma R. Salunkhe

Received 6 January 2017; Accepted 20 February 2017; Published 12 March 2017

Academic Editor: Arun K. Sangaiah

Copyright © 2017 Uma R. Salunkhe and Suresh N. Mali. 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.


In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.