Toward Privacy-Preserving Training of Generative AI Models for Network Traffic Classification
Year: 2025
Type: article
Abstract: Synthetic traffic traces are useful for training traffic classifiers in privacy-constrained environments. Generative Artificial Intelligence (GAI) models are blossoming as a solution to avoid the sharing of real data and the lack of datasets. Never the less ,privacy concerns about GAI are often under-estimated. Therefore, an approach to mitigate the data leakage of a GAI is presented in this paper, with a minimum impact on the utility of synthetic traffic traces for downstream applications. For example, training a Machine Learning (ML) traffic classifier on synthetic traffic traces results in an average accuracy loss of no more than 13% concerning training on a real dataset.
Author: Ciro Guida
Institution: University of Naples Federico II
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4408556798