# Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection

arXiv: https://arxiv.org/abs/2404.10800 · HTML: https://mlsanity.com/paper/2404.10800/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Kymatio: Scattering Transforms in Python](https://mlsanity.com/paper/1812.11214/) (2018)
- [E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT](https://mlsanity.com/paper/2103.16329/) (2021)
- [Towards a Standard Feature Set for Network Intrusion Detection System Datasets](https://mlsanity.com/paper/2101.11315/) (2021)
- [Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks](https://mlsanity.com/paper/2207.06819/) (2022)
- [Automating Botnet Detection with Graph Neural Networks](https://mlsanity.com/paper/2003.06344/) (2020)
- [Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing](https://mlsanity.com/paper/2201.09020/) (2022)
- [On Generalisability of Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2205.04112/) (2022)
- [A parameter-free graph reduction for spectral clustering and SpectralNet](https://mlsanity.com/paper/2302.13165/) (2023)

## Built on by (most cited first, 1 of 1)

- [AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders](https://mlsanity.com/paper/2511.17113/) (2025)

## Abstract (arXiv)

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.