Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection
This work addresses network security by enhancing anomaly detection in intrusion detection systems, representing an incremental advancement with specific gains.
The paper tackles anomaly detection in network intrusion detection systems by introducing two novel methods: STEG, which uses scattering transform for multi-resolution analysis of edge features, and an improved node representation method starting with Node2Vec. The results show significant performance improvements over state-of-the-art methods on benchmark datasets.
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.