NFlowGAN: High-Utility Privacy-Preserving Network Flow Synthesis Based on GAN

The sensitivity of network traffic data has led to the scarcity of public traffic datasets, hindering the development of data-driven research in this field. Researchers proposed publishing synthetic network traffic instead of the original dataset. However, existing traffic synthesis methods are inadequate in data utility and seldom consider privacy protection. For this reason, we propose NFlowGAN for high-utility privacy-preserving network flow synthesis. We introduce spectral normalization in the network structure to improve training stability, thus improving the data utility. In addition, we add a Gaussian noise layer to the discriminator of NFlowGAN to provide higher privacy guarantees for the synthesized flow. The experimental evaluation results on the Darknet2020 dataset demonstrate that our proposed NFlowGAN achieves a significant improvement in data utility with privacy preservation compared to the two baselines. The synthesized high-utility dataset can be widely shared for research and educational purposes.