Security Enrichment in Intrusion Detection System Using Classifier Ensemble

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. © 2017 uma r. salunkhe and suresh n. mali.