Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network

arXiv:2403.01501v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have garnered intensive attention for Network Intrusion Detection System (NIDS) due to their suitability for representing the network traffic flows. However, most present GNN-based methods for NIDS are supervised or semi-supervised. Network flows need to be manually annotated as supervisory labels, a process that is time-consuming or even impossible, making NIDS difficult to adapt to potentially complex attacks, especially in large-scale real-world scenarios. The existing GNN-based self-supervised methods focus on the …

arxiv cs.cr cs.lg detection graph graph neural network network neural network self-supervised learning supervised learning type