  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.
