A tale of two methods: unveiling the limitations of GAN and the rise of bayesian networks for synthetic network traffic generation - Télécom SudParis
Communication Dans Un Congrès Année : 2024

A tale of two methods: unveiling the limitations of GAN and the rise of bayesian networks for synthetic network traffic generation

Résumé

The evaluation of network intrusion detection systems requires a sufficient amount of mixed network traffic, i.e., composed of both malicious and legitimate flows. In particular, obtaining realistic legitimate traffic is hard. Synthetic network traffic is one of the tools to respond to insufficient or incomplete real-world datasets. In this paper, we only focus on synthetically generating high-quality legitimate traffic and we do not delve into malicious traffic generation. For this specific task, recent contributions make use of advanced machine learning-driven approaches, notably through Generative Adversarial Networks (GANs). However, evaluations of GAN-generated data often disregards pivotal attributes, such as protocol adherence. Our study addresses the gap by proposing a comprehensive set of metrics that assess the quality of synthetic legitimate network traffic. To illustrate the value of these metrics, we empirically compare advanced network-oriented GANs with a simple and yet effective probabilistic generative model, Bayesian Networks (BN). According to our proposed evaluation metrics, BNbased network traffic generation outperforms the state-ofthe-art GAN-based opponents. In our study, BN yields substantially more realistic and useful synthetic benign traffic and minimizes the computational costs simultaneously.
Fichier principal
Vignette du fichier
tmpy9y3gxhu_AsiaCCS_Revised.pdf (415.53 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04871298 , version 1 (07-01-2025)

Identifiants

Citer

Adrien Schoen, Gregory Blanc, Pierre-François Gimenez, Yufei Han, Frédéric Majorczyk, et al.. A tale of two methods: unveiling the limitations of GAN and the rise of bayesian networks for synthetic network traffic generation. 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), Jul 2024, Vienna, Austria. pp.273-286, ⟨10.1109/EuroSPW61312.2024.00036⟩. ⟨hal-04871298⟩

Altmetric

Partager

More