MirageNet - Towards a GAN-based Framework for Synthetic Network Traffic Generation

With the emergence of machine learning technology that supports the development of synthetic models, many new use cases and challenges are emerging in the fields of computer vision and security. The main model behind this technology is Generative Adversarial Networks (GANs), with their ability to model unknown distributions accurately and perform well in generating synthetic data such as images and videos. However, the application of this technology by the networking community has been lacking. Given this motivation, we introduce MirageNet; our vision for a GAN-based synthetic network traffic generation framework, which can automatically create synthetic network models of protocols, applications, and devices. With the potential to build many applications for privacy, security, and network optimization. In this paper, we present MiragePkt; the first component of MirageNet. It is a GAN-based model to synthetically generate network packets. We describe the different challenges, limitations, and solutions for generating synthetic network packets. Finally, we validate and evaluate the performance of our framework with the synthesizing DNS packets.