SummaryThe rapid growth of the Internet of Things (IoT) in our daily life has recently received attention from hackers in releasing novel attacks. This is because the existing traditional Intrusion Detection System (IDS) uses an alert‐based approach that cannot detect new emerging attacks, making it unfeasible for devices with limited resources. The study presents the deployment of a Parallel Deep Neural Network (P‐DNN) IDS framework to improve the detection rate and resource constraint issues. To validate the framework for detecting the latest IoT vulnerabilities, we generated a proprietary dataset created under the IoT environment. The experimental results reveal that the P‐DNN IDS framework proposed in this study outperformed the K‐nearest Neighbour (KNN), Support Vector Machine (SVM), and Naive Bayes algorithms with respect to performance metrics. In addition, a comparative analysis of the P‐DNN framework with Snort and Suricata IDS results demonstrates a significant reduction in CPU consumption, memory utilization, and processing time. The results of this investigation indicate that the deployment of the P‐DNN framework has the capability to detect vulnerabilities in resource‐constraint IoT environments effectively.