Advancing Network Intrusion Detection: Integrating Graph Neural Networks
  with Scattering Transform and Node2Vec for Enhanced Anomaly Detection
Abdeljalil Zoubir Badr Missaoui
In this paper, we present two novel methods in Network Intrusion Detection
Systems (NIDS) using Graph Neural Networks (GNNs). The first approach,
Scattering Transform with E-GraphSAGE (STEG), utilizes the scattering transform
to conduct multi-resolution analysis of edge feature vectors. This provides a
detailed representation that is essential for identifying subtle anomalies in
network traffic. The second approach improves node representation by initiating
with Node2Vec, diverging from standard methods of using uniform values, thereby
capturing a more accurate and holistic network picture. Our methods have shown
significant improvements in performance compared to existing state-of-the-art
methods in benchmark NIDS datasets.