Summary
In this chapter, we explored the use of GNNs for detecting anomalies in a new dataset, the CIDDS-001 dataset. First, we preprocessed the dataset and converted it into a graph representation, allowing us to capture the complex relationships between the different components of the network. We then implemented a heterogeneous GNN with GraphSAGE operators. It captured the heterogeneity of the graph and allowed us to classify the flows as benign or malicious.
The application of GNNs in network security has shown promising results and opened up new avenues for research. As technology continues to advance and the amount of network data increases, GNNs will become an increasingly important tool for detecting and preventing security breaches.
In Chapter 17, Recommending Books Using LightGCN, we will explore the most popular application of GNNs with recommender systems. We will implement a lightweight GNN on a large dataset and produce book recommendations for given users.
