Deutsch Intern
    Data Science Chair


    Publications by Andreas Hotho

    These publications are hosted by BibSonomy.

    Flow-based Network Traffic Generation using Generative Adversarial Networks.

    Ring, Markus; Schlör, Daniel; Landes, Dieter; Hotho, Andreas in CoRR 2018 .

    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.
    Further Information
    Tags 2018  GAN  flow  ids  myown  network  traffic