Anomaly Detection in IoT Networks using Graph Neural Networks

The importance of anomaly detection in IoT device networks has gained much significance in enhancing security, enabling reliable monitoring and reducing potential threats. A deep learning model for detecting different types of cyberattacks on various devices using the autoencoder model and distinguishing normal patterns of traffic from malicious ones by using the RT-IoT2022 dataset is proposed. The proposed scheme uses GNN to perceive the environment for the identification and classification of real-time cyberattacks. It makes IoT devices more secure, stable and achieves high accuracy.The proposed GNN-based algorithm demonstrated an impressive 99% accuracy in the identification of anomalies in IoT networks, providing strong security and threat avoidance.