Traffic generation is an important tool for many data network activities such as simulation, planning and provi-sioning. Such a generation could be based on theoretical models when available; however, we believe that mimicking ground truth traffic previously collected is a much more generic solution. It provides a comprehensive tool to accommodate the various characteristics of current or future communication systems (IoT, 5G service models,…). The Deep Learning toolbox is now mature enough to provide the right tools to capture and reproduce the multi-scale factors of network traffic. In this paper, we propose a new architecture, called NeC-STGen, based on Deep learning models such as: Variational Autoencoders (VAE) and Recurrent Neural Network (RNN) for the generation of various network traffic. NeCSTGen can generate live traffic that accurately reproduces the original behaviour at the packet, flow and aggregate levels. Our approach allows an innovative fine-grained understanding and generation without the need to understand, in depth, how the protocol to be generated works. Our reproducible architecture generates data that can be exported in a. pcap file to be used for any purpose.