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With the increase in popularity of Internet of Things (IoT) and the rise in interconnected devices, the need to foster effective security mechanism to handle vulnerabilities and risks in IoT networks has become evident. Security mechanisms, such as intrusion detection system (IDS), are designed and deployed in IoT network environment to ensure security and prevent unauthorized access to system and resources. Moreover, there have been efforts to design IDS using various deep learning (DL) techniques, as these techniques possess the intriguing characteristic of representing data with high abstraction. However, the intrusion detection data sets used in literature possess imbalance class distribution, which is one of the challenging issues in developing coherent and potent intrusion detection and classification system. In this article, we aim to address class imbalance problem using ensemble learning approach, namely …