%0 Journal Article %1 journals/corr/abs-1810-07795 %A Ring, Markus %A Schlör, Daniel %A Landes, Dieter %A Hotho, Andreas %D 2018 %J CoRR %K %T Flow-based Network Traffic Generation using Generative Adversarial Networks. %U http://dblp.uni-trier.de/db/journals/corr/corr1810.html#abs-1810-07795 %V abs/1810.07795 %X 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.

@article{journals/corr/abs-1810-07795, abstract = {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.}, added-at = {2023-12-13T06:55:59.000+0100}, author = {Ring, Markus and Schlör, Daniel and Landes, Dieter and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2ec09bc6976e094dc873d7c0d3391c501/admin}, ee = {http://arxiv.org/abs/1810.07795}, interhash = {b362e4932b7a745d5a8dffc7ebdfb288}, intrahash = {ec09bc6976e094dc873d7c0d3391c501}, journal = {CoRR}, keywords = {}, timestamp = {2023-12-13T06:55:59.000+0100}, title = {Flow-based Network Traffic Generation using Generative Adversarial Networks.}, url = {http://dblp.uni-trier.de/db/journals/corr/corr1810.html#abs-1810-07795}, volume = {abs/1810.07795}, year = 2018 }