Generative, High-Fidelity Network Traces - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Generative, High-Fidelity Network Traces

Résumé

Recently, much attention has been devoted to the development of generative network traces and their potential use in supplementing real-world data for a variety of data-driven networking tasks. Yet, the utility of existing synthetic traffic approaches are limited by their low fidelity: low feature granularity, insufficient adherence to task constraints, and subpar class coverage. As effective network tasks are increasingly reliant on raw packet captures, we advocate for a paradigm shift from coarse-grained to fine-grained traffic generation compliant to constraints. We explore this path employing controllable diffusion-based methods. Our preliminary results suggest its effectiveness in generating realistic and fine-grained network traces that mirror the complexity and variety of real network traffic required for accurate service recognition. We further outline the challenges and opportunities of this approach, and discuss a research agenda towards text-to-traffic synthesis.
Fichier principal
Vignette du fichier
nopaper.pdf (248.88 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04267389 , version 1 (01-11-2023)

Identifiants

Citer

Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Paul Schmitt, Francesco Bronzino, et al.. Generative, High-Fidelity Network Traces. Twenty-Second ACM Workshop on Hot Topics in Networks, Nov 2023, Cambridge (MA), US, United States. ⟨10.1145/3626111.3628196⟩. ⟨hal-04267389⟩
100 Consultations
317 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More