[2603.25507] Lightweight GenAI for Network Traffic Synthesis: Fidelity, Augmentation, and Classification

Computer Science > Networking and Internet Architecture arXiv:2603.25507 (cs) [Submitted on 26 Mar 2026] Title:Lightweight GenAI for Network Traffic Synthesis: Fidelity, Augmentation, and Classification Authors:Giampaolo Bovenzi, Domenico Ciuonzo, Jonatan Krolikowski, Antonio Montieri, Alfredo Nascita, Antonio Pescapè, Dario Rossi View a PDF of the paper titled Lightweight GenAI for Network Traffic Synthesis: Fidelity, Augmentation, and Classification, by Giampaolo Bovenzi and 5 other authors View PDF HTML (experimental) Abstract:Accurate Network Traffic Classification (NTC) is increasingly constrained by limited labeled data and strict privacy requirements. While Network Traffic Generation (NTG) provides an effective means to mitigate data scarcity, conventional generative methods struggle to model the complex temporal dynamics of modern traffic or/and often incur significant computational cost. In this article, we address the NTG task using lightweight Generative Artificial Intelligence (GenAI) architectures, including transformer-based, state-space, and diffusion models designed for practical deployment. We conduct a systematic evaluation along four axes: (i) (synthetic) traffic fidelity, (ii) synthetic-only training, (iii) data augmentation under low-data regimes, and (iv) computational efficiency. Experiments on two heterogeneous datasets show that lightweight GenAI models preserve both static and temporal traffic characteristics, with transformer and state-space mode...