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The introduction of the Internet of Things has led to the connectivity of millions of devices with less human interaction. This demand in connectivity has resulted in a surge in network attacks as IoT is susceptible to several cyberattacks. Due to their resource-constrained nature, traditional security mechanisms are inappropriate for securing IoT systems. Hence, the need for pervasive security mechanisms that are robust to mitigate attacks and secure IoT networks. One of the emerging potential solutions to network security is Machine Learning (ML). Recently, ML has been applied to mitigate cybersecurity threats in Cyber-Physical Systems (CPS). This paper presents a hybrid ML model for the efficient and effective detection of anomalies in IoT systems. The proposed model combines Random Forest algorithm, XGB, KNN and two decision tress with equal weights assigned to enhance the detection of anomalies in IoT …