GNN-based Anomaly Detection for Encoded Network Traffic
This addresses network security for IT professionals, but it is incremental as it applies existing GNN methods to a new domain with limited prior research.
The paper tackles anomaly detection in network traffic by using Graph Neural Networks (GNNs) with encoded features from network flow data, reporting improved performance but without specific numerical results.
The early research report explores the possibility of using Graph Neural Networks (GNNs) for anomaly detection in internet traffic data enriched with information. While recent studies have made significant progress in using GNNs for anomaly detection in finance, multivariate time-series, and biochemistry domains, there is limited research in the context of network flow data. In this report, we explore the idea that leverages information-enriched features extracted from network flow packet data to improve the performance of GNN in anomaly detection. The idea is to utilize feature encoding (binary, numerical, and string) to capture the relationships between the network components, allowing the GNN to learn latent relationships and better identify anomalies.