Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis

Artificial Intelligence (AI) research often aims to develop models that generalize reliably across complex datasets, yet this remains challenging in fields where data is scarce, intricate, or inaccessible. This paper introduces a novel approach leveraging three generative models of varying complexity to synthesize one of the most demanding structured datasets: Malicious Network Traffic. Our approach transforms numerical data into text, reframing data generation as a language modeling task, which enhances data regularization and significantly improves generalization and the quality of the synthetic data. Extensive statistical analyses demonstrate that our method surpasses state-of-the-art generative models in producing high-fidelity synthetic data. Additionally, we conduct a comprehensive study on synthetic data applications, effectiveness, and evaluation strategies, offering valuable insights into its role across various domains. Our code and pre-trained models are openly accessible at https://github.com/Moe-Zbeeb/Exploring-the-landscape-for-generative-models-for-specialized-data-generation, enabling further exploration and application of our methodology. Index Terms: Data synthesis, machine learning, traffic generation, privacy-preserving data, generative models.