Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization’s network infrastructure. Thus, a network intrusion detection system is required to detect attacks in network traffic. This paper proposed 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. The main originality of the proposed approach is based on three main parts: pre-processing phase, classification phase and combining phase. 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.