Published Paper Details:

Graph Neural Networks (GNNs) are revolutionising anomaly detection in wireless networks by effectively utilising graph-structured data to model intricate relationships among network components. Unlike traditional techniques, GNNs excel in capturing both node-level and edge-level interactions, enabling the detection of subtle irregularities that may signify security breaches or performance issues. Their ability to learn dynamic graph representations through message-passing and feature aggregation enhances detection accuracy and scalability. GNN-based solutions address critical challenges in wireless networks, such as dynamic topology, high-dimensional data, and scalability. By strengthening network reliability, optimising resource allocation, and enhancing security, GNNs pave the way for robust monitoring and management of large-scale wireless systems. As wireless networks continue to grow in complexity, GNNs offer a promising approach to anomaly detection, fostering advancements in automation and intelligent network analysis. This paper explores the methodologies, applications, and challenges of applying GNNs for anomaly detection in wireless networks, highlighting their transformative potential in the field.