Authors - Nasika Ijaz, Farooque Azam, Saliha Ejaz, Muhammad Waseem Anwar Abstract - Anomaly detection in dynamic cybersecurity networks has been a promising problem that has been addressed using Graph Neural Networks (GNNs). Today’s network topologies are too difficult to handle for traditional methods; the topologies are too dynamic and complex. The main contribution of this study is the evaluation of three GNN models, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and RepographGAN, in terms of effectiveness to detect anomalies in dynamic network environments. Conventional anomaly detection techniques such as logistic regression, support vectors machines (SVM) and decision trees are compared against the models. The results demonstrate that RegraphGAN is superior to the other models in terms of accuracy, precision, recall, F1 score, and AUC-ROC, and is thus very effective at identifying anomalies. However, as computing resources are required for it, a compromise between performance and computing resources is found. Despite the lower accuracy of GCN and GAT, these provide more computationally efficient solutions that are appropriate for real time deployment constraints in such resource constrained environments. The findings provide a basis for future research that can optimize scalability and computational efficiency for large scale applications and in the context suggest the use of GNNs for improving cybersecurity systems.
Open Zoom