The increasing interconnectivity of Internet-of-Things (IoT) has exposed them to diverse cyber threats and adversarial attacks, distributed denial-of-service (DDoS) attacks, spoofing and man-in-the-middle intrusions, malware injections, ransomware, and adversarial machine learning exploits. To detect these attacks, this research leverages Graph Neural Networks (GNNs) for intrusion detection and attack analysis by exploiting the graph’s intrinsic structure of communication networks and sessions. We propose advanced GNN-based models that extract high-dimensional features from IoT networks and enable in-depth analysis of packets. By representing IoT networks as graphs, GNNs effectively capture the intricate interactions and dependencies among network components. The proposed models were trained on three distinct datasets, namely ToNIoT, NFBoTIoT, and GraSecIoT, to perform detection tasks, including binary classification to differentiate normal from malicious behavior and multi-class classification to identify one or more underlying attacks. The experimental results validate the effectiveness of graph neural networks in detecting malicious activities and categorizing attack types, thereby offering a robust solution for securing IoT environments.