[2507.20115] Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion

Computer Science > Networking and Internet Architecture arXiv:2507.20115 (cs) [Submitted on 27 Jul 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion Authors:Gongli Xi, Ye Tian, Yannan Hu, Yuchao Zhang, Yapeng Niu, Xiangyang Gong View a PDF of the paper titled Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion, by Gongli Xi and 4 other authors View PDF HTML (experimental) Abstract:In response to Distributed Denial of Service (DDoS) attacks, recent research efforts increasingly rely on Machine Learning (ML)-based solutions, whose effectiveness largely depends on the quality of labeled training datasets. To address the scarcity of such datasets, data augmentation with synthetic traces is often employed. However, current synthetic trace generation methods struggle to capture the complex temporal patterns and spatial distributions exhibited in emerging DDoS attacks. This results in insufficient resemblance to real traces and unsatisfied detection accuracy when applied to ML tasks. In this paper, we propose Dual-Stream Temporal-Field Diffusion (DSTF-Diffusion), a multi-view, multi-stream network traffic generative model based on diffusion models, featuring two main streams: The field stream utilizes spatial mapping to bridge network data characteristics with pre-trained realms of stable diffusion models, effectively translating complex network interactions into ...