Method and arrangement for detecting anomalies in network data traffic

Abstract

The present disclosure relates to a method and an apparatus for training a model for detecting anomalies in network data traffic between devices in a first part of a network and devices in a second part of the network. The method comprises collecting feature samples of network data traffic at a monitoring point between a first and a second part of the network, and training the model for detecting anomalies on the collected feature samples using a plurality of anomaly detection, AD, trees. The training comprises creating the plurality of AD trees using respective subsets of the collected feature samples, at least some of the AD tree comprising subspace selection nodes and anomaly-catching nodes to a predetermined AD tree depth limit. Each subspace selection node is arranged to bisect a set of feature samples reaching the subspace selection node to at least one anomaly-catching node when a number of feature samples leaving the subspace selection node for the at least one anomaly-catching node is below a predetermined threshold. The disclosure also relates to a method and an apparatus for anomalies in network data traffic using said model.