Evaluation of Anomaly Detection for Cybersecurity Using Inductive Node Embedding with Convolutional Graph Neural Networks

In the face of continuous cyberattacks, many scientists have proposed machine learning-based network anomaly detection methods. While deep learning effectively captures unseen patterns of Euclidean data, there is a huge number of applications where data are described in the form of graphs. Graph analysis have improved detecting anomalies in non-Euclidean domains, but it suffered from high computational cost. Graph embeddings have solved this problem by converting each node in the network into low dimensional representation, but it lacks the ability to generalize to unseen nodes. Graph convolution neural network methods solve this problem through inductive node embedding (inductive GNN). Inductive GNN shows better performance in detecting anomalies with less complexity than graph analysis and graph embedding methods.