Generative adversarial networks (GANs) are one of the major ML techniques for data augmentation and classification, in the field of image processing, computer vision and natural language processing. However, in the field of data networks and protocols the use of GANs for data generation and classification (at packet level) is very limited or relatively new. Although, GANs specific properties and characteristics can be highly relevant in this context (unsupervised technique). This limitation, is even more critical if we consider network protocols or communication oriented protocols such as SIP VoIP To address this problem, we propose “SIP-GAN” an extension and adaptation of GANs model for SIP, aiming to process and generate SIP traffic at packet level. The proposed generic model includes an encoder, a generator, and a decoder The encoder extracts information from pcap data, associates and converts these SIP data into a GAN image representation. The generator is based on a DCGAN model, that generates new SIP dataset from each extracted image. The decoder combines the generated images and reconstruct a valid pcap file (SIP file). A specific testbed, with a formal and practical analysis, demonstrate the validity of the generated data, from the SIP-GAN model. Also, the experimental and performance results are globally satisfactory, showing the relevance of our proposed SIP-GAN based traffic generator in this context.