首页|Toward Privacy-Preserving Training of Generative AI Models for Network Traffic Classification
Toward Privacy-Preserving Training of Generative AI Models for Network Traffic Classification
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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. Nevertheless, privacy concerns about GAI are often underestimated. 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.
Ciro Guida
展开 >
University of Naples Federico Ⅱ, Italy||University of Bergamo, Italy
NETL
NSTL
