Detecting Anomalies Using Heterogeneous GNNs
In machine learning, anomaly detection is a popular task that aims to identify patterns or observations in data that deviate from the expected behavior. This is a fundamental problem that arises in many real-world applications, such as detecting fraud in financial transactions, identifying defective products in a manufacturing process, and detecting cyber attacks in a computer network.
GNNs can be trained to learn the normal behavior of a network and then identify nodes or patterns that deviate from that behavior. Indeed, their ability to understand complex relationships makes them particularly appropriate to detect weak signals. Additionally, GNNs can be scaled to large datasets, making them an efficient tool for processing large amounts of data.
In this chapter, we will build a GNN application for anomaly detection in computer networks. First, we will introduce the CIDDS-001 dataset, which contains attacks and benign traffic in a...
