Ensemble Learning Approach for Intrusion Detection Systems in Industrial Internet of Things

The Industrial Internet of Things (IIoT) has completely changed how industrial processes are carried out, resulting in higher production and efficiency. Strong intrusion detection systems (IDS) must be implemented inside IIoT systems because of the increasing risk of security attacks brought on by increased connection and communication channels. In this work, we provide a combined strategy for successful IDS in IIoT systems based on data mining and machine learning/deep learning processes. Our suggested approach integrates a number of strategies, including anomaly detection, feature selection, and ensemble learning, to precisely identify and categorize distinct sorts of intrusion attempts. We conduct extensive trials on publicly accessible datasets (Edge IIoT) to show the efficacy of our method, and the results outperform current state-of-the-art methods.