Intrusion Detection using an Ensemble of Classification Methods
Muthukumarasamy Govindarajan · 2012
Abstract—one of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for intrusion detection. Due to increasing incidents of cyber attacks, building effective intrusion detection systems are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this research work, new hybrid classification method is proposed using classifiers in a heterogeneous environment using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using a Radial Basis Function (RBF) and Support Vector Machine (SVM). Here, modified training sets are formed by resampling from original training set; classifiers