Flow-based data sets are necessary for evaluating network-based intrusion de-
tection systems (NIDS). In this work, we propose a novel methodology for gener-
ating realistic flow-based network traffic. Our approach is based on Generative
Adversarial Networks (GANs) which achieve good results for image generation.
A major challenge lies in the fact that GANs can only process continuous at-
tributes. However, flow-based data inevitably contain categorical attributes
such as IP addresses or port numbers. Therefore, we propose three different
preprocessing approaches for flow-based data in order to transform them into
continuous values. Further, we present a new method for evaluating the gener-
ated flow-based network traffic which uses domain knowledge to define quality
tests. We use the three approaches for generating flow-based network traffic
based on the CIDDS-001 data set. Experiments indicate that two of the three
approaches are able to generate high quality data.