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The capacity to identify breaches and malicious activity inside the Internet of Things (IoT) networks is important for network infrastructure resilience as the dependence on IoT devices and services grows. Intrusion detection systems (IDS) are basic components of network security. IDSs monitor and analyze the activity of a system in a network to identify intrusions. Existing intrusion detection systems (IDS) gather and utilize large amounts of data with irrelevant, unnecessary, and unsuitable characteristics, resulting in long detection times and low accuracy. In this paper, we present an IDS model based on a Random Forest (RF) classifier. NSL-KDD dataset is used to test the performance of the model and the satisfying performance is obtained in terms of accuracy, detection rate, and false alarm rate. The proposed model has attained an average accuracy of 99.3% and 98% for binary classification and multiclass …