Performance Evaluation of Ensemble Classifiers for Anomaly Detection in IoT Environment

Due to the reliance of the modern community on networks, the significance of efficient intrusion detection systems (IDS) cannot be ignored. As network intrusions are frequently and critically emerging, exhibiting unknown patterns, smart systems practicing machine learning approaches, have been readily explored to deal with certain issues. In this paper, we acknowledge a different ensemble-oriented approaches for detecting numerous types of outliers. The assets of ensembled techniques over common machine learning mechanisms is the competence to train an unlabeled data. Thus, they are applicable for observing unfamiliar attacks. The pivotal objective of the proposed scheme is to train and test the data for achieving high accuracy rate and minimal false positive rate. The experiment is implemented on NSL-KDD dataset which show that how effectively ensemble algorithms generate highly accurate models with low false positive rates. And outperforms in case of predicting unknown anomalies.