Federated Learning For Network Attack Detection Using Attention-based Graph Neural Networks

This article discusses the development of a federated learning network intrusion collaborative detection framework, FedGAT, which allows for collaborative model training while ensuring data privacy and security on distributed devices. It also improves learning efficiency, interpretability of decisions, and the ability to capture complex patterns in graph-structured data. The framework provides a technical route for cross-level sharing of network device traffic data and enables multi-dimensional collaborative detection of intrusions. The article also highlights the limitations of traditional machine learning methods in the face of new network intrusion detection scenarios and the potential of using big data technology and deep learning for IDS.