Graph Neural Networks for Network Intrusion Detection

Network traffic is inherently relational — devices communicate in graph patterns. Graph Neural Networks (GNNs) are naturally suited to model these relationships for security analysis.

Why GNNs for Network Security?

Traditional ML approaches treat network flows as independent samples, missing the rich structural information in communication patterns. GNNs capture: - **Node features**: Device behavior profiles, open ports, OS fingerprints - **Edge features**: Protocol, byte count, connection duration, TLS fingerprints - **Graph structure**: Network topology, community detection, centrality measures

Implementation

Using PyTorch Geometric, we build a GCN-based anomaly detector trained on benign network traffic and evaluated on CIC-IDS-2017 and UNSW-NB15 datasets. The model achieves 98.7% AUC in detecting lateral movement patterns that signature-based IDS miss.