Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis
This addresses data scarcity and privacy issues in network security, offering a novel method for specialized domains, though it is incremental in its application of existing generative techniques.
The paper tackles the challenge of generating synthetic data for scarce or complex datasets, specifically malicious network traffic, by transforming numerical data into text and using generative models, achieving state-of-the-art results in high-fidelity data synthesis.
Artificial Intelligence (AI) research often aims to develop models that can generalize reliably across complex datasets, yet this remains challenging in fields where data is scarce, intricate, or inaccessible. This paper introduces a novel approach that leverages three generative models of varying complexity to synthesize one of the most demanding structured datasets: Malicious Network Traffic. Our approach uniquely transforms numerical data into text, re-framing data generation as a language modeling task, which not only enhances data regularization but also 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 Github, enabling further exploration and application of our methodology. Index Terms: Data synthesis, machine learning, traffic generation, privacy preserving data, generative models.