Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection

TL;DR

This paper introduces two innovative GNN-based methods for network intrusion detection, leveraging scattering transform and improved node representation to enhance anomaly detection accuracy in benchmark datasets.

Contribution

The paper presents two novel methods combining scattering transform and advanced node embedding for improved anomaly detection in network traffic.

Findings

Significant performance improvements over state-of-the-art methods

Effective multi-resolution analysis of edge features

Enhanced node representation for better anomaly detection

Abstract

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.