Towards a Global Anomaly Detector in High-speed Networks Where Traffic Sampling is a Necessity

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.