Effective Intrusion Detection System Using Classifier Ensembles

The problem of network intrusion detection poses innumerable challenges to the research community, industry, and commercial sectors. Moreover, the persistent attacks occurring on the cyber-threat landscape compel researchers to devise robust approaches in order to address the recurring problem. Given the presence of huge web traffic, standard machine learning approaches are rather inefficient if adapted in network intrusion detection areas. Instead, a hybrid multiple classifier model when attempted enhances the performance henceforth leading to valid predictions. Thus, novel ensemble approaches are presented in this research work that involves bagged homogeneous classifier ensembles and arcing of heterogeneous ensembles. Then the classification performances of classifier models are assessed using accuracy. Here, classifier ensemble is built using base classifiers such as RBF and SVM. The feasibleness and the advantages of the proposed approaches are illustrated with the help of existing intrusion detection dataset. pre-processing phase, classification phase and combining phase are the three major phases of this proposed method. A broad series of analogous experiments are done for standard dataset of intrusion detection. Furthermore, comparisons with previous work on standard dataset of intrusion detection are also exhibited. The experimental outcomes demonstrate that this proposed ensemble approaches are competitive.