Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection

• URL: http://arxiv.org/abs/2404.10800v3
• Date: Wed, 24 Apr 2024 12:43:30 GMT
• Title: Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection
• Authors: Abdeljalil Zoubir, Badr Missaoui,
• Abstract summary: 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. The second approach improves node representation by initiating with Node2Vec, diverging from standard methods of using uniform values.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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