Network anomaly detection is a core research problem. In a previous paper, we proposed the concept of "autoencoder losses transfer learning." This approach normalizes autoencoder losses in different model deployments, and uses a global detection model to detect and classify threats in a generalized way that is agnostic to the specific network deployment, allowing to learn from significantly d ifferent n etworks. W hile t he previous paper provided initial simulations and evaluation results, this paper will present new results of an extensive empirical study of the proposed approach over additional datasets.

A classic long-term challenge is detecting anomalies in computer networks. Despite almost every kind of model architecture, previous works focused on analyzing the complete network traffic, which is becoming less applicable for large networks due to processing overheads. This poster presents a work in progress that transforms the computer network into a graph neural network (GNN) by analyzing only a small fraction of network traffic and detecting anomalies.