PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security

arXiv:2406.13365v1 Announce Type: new
Abstract: Recent works have demonstrated the potential of Graph Neural Networks (GNN) for network intrusion detection. Despite their advantages, a significant gap persists between real-world scenarios, where detection speed is critical, and existing proposals, which operate on large graphs representing several hours of traffic. This gap results in unrealistic operational conditions and impractical detection delays. Moreover, existing models do not generalize well across different networks, hampering their deployment in production environments. To address these issues, we …

abstract advantages arxiv cs.ai cs.cr cs.lg detection gap gnn graph graph neural network graph neural networks graphs network networks network security neural network neural networks potential practical proposals security speed temporal traffic type world