GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detection

ArXi:2510.07285v3 Announce Type: replace-cross The escalating complexity of network threats and the inherent class imbalance in traffic data present formidable challenges for modern Intrusion Detection Systems (IDS). While Graph Neural Networks (GNNs) excel in modeling topological structures and Temporal Convolutional Networks (TCNs) are proficient in capturing time-series dependencies, a framework that synergistically integrates both while explicitly addressing data imbalance remains an open challenge. This paper.