Optimizing Network Intrusion Detection Performance with GNN-Based Feature Selection

The rapid evolution of AI-driven cybersecurity solutions has led to increasingly complex network infrastructures, which in turn increases their exposure to sophisticated threats. This study proposes a Graph Neural Network (GNN)-based feature selection strategy specifically tailored for Network Intrusion Detection Systems (NIDS). By modeling feature correlations and leveraging their topological relationships, this method addresses challenges such as feature redundancy and class imbalance. Experimental analysis using the KDDTest+ dataset demonstrates that the proposed model achieves 98.5% detection accuracy, showing notable gains in both computational efficiency and minority class detection. Compared to conventional machine learning methods, the GNN-based approach exhibits a superior capability to adapt to the dynamics of evolving cyber threats. The findings support the feasibility of deploying GNNs for scalable, real-time anomaly detection in modern networks. Furthermore, key predictive features, notably f35 and f23, are identified and validated through correlation analysis, thereby enhancing the model’s interpretability and effectiveness.