Advancing Network Intrusion Detection: Integrating Graph Neural Networks with Scattering Transform and Node2Vec for Enhanced Anomaly Detection

arXiv:2404.10800v2 Announce Type: replace-cross
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 …

abstract analysis anomaly anomaly detection arxiv cs.ai cs.cr cs.lg detection edge feature gnns graph graph neural networks network networks neural networks node2vec novel paper resolution systems type