2Pack-GAN: Exploring Transfer Learning to Fine-Tune Generative Adversarial Networks for Network Packet Generation

Network datasets are essential resources to evaluate the effectiveness of new technologies in diverse network environments. Unfortunately, these datasets are often not publicly available or lack the necessary density and diversity for thorough testing. Generative Adversarial Networks (GANs) have shown promise in generating realistic synthetic data. Transfer Learning (TL), which transfers knowledge from one domain to another, is another technique that potentially enhances data generation. This paper combines such techniques in a novel framework capable of generating synthetic network data tailored to specific desired protocol types. A GAN is pre-trained on an available dataset with substantial size and diversity corresponding to a source protocol. Then, exploring a TL method, the GAN undergoes fine-tuning using a smaller target protocol dataset. This fine-tuning allows the GAN to generate an augmented dataset containing relevant samples from the target domain. Developed experiments include two different data generation scenarios: i) Intra-protocol: transferring knowledge from datasets with different characteristics, considering the same source and target protocols; ii) Inter-protocol: transferring knowledge between protocols from different layers of the ISO-OSI reference model. The obtained results demonstrate that the proposed GAN and TL framework effectively generates high-quality synthetic traffic, featuring a significant rate of well-formed packets; a high percentage of packets with an appropriate query response; and a considerable similarity between the generated and real packets - measured by the FID (Fréchet Inception Distance) - compared to standard GANs without fine-tuning.