Flow-based network traffic generation using Generative Adversarial Networks

doi:10.1016/j.cose.2018.12.012

Flow-based network traffic generation using Generative Adversarial Networks

2019-05-01

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DOI：10.1016/j.cose.2018.12.012

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Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating 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 attributes. 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 generated 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. (C) 2018 Elsevier Ltd. All rights reserved.

Keywords:

GANs

TTUR WGAN-GP

NetFlow

Generation

IDS

Flow-based network traffic generation using Generative Adversarial Networks

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