Communication Dans Un Congrès Année : 2025

A Simple yet Accurate Autoadaptive Model of Network Traffic for Detection of Attacks on Low Latency Services

Résumé

This paper addresses the problem of detection of attacks in computer networks. More precisely, we consider attacks on emerging low-latency services, which typically require a specific traffic management system. We present a simple yet very efficient hybrid method that takes advantage of both autoencoders and transformer models. The original method is compared with the current stateof-the-art on a large real-life dataset of network traffic to show the relevance of the proposed approach, especially for low falsepositive rates. A quick ablation analysis shows that the efficiency of the method relies on the combined use of the two approaches jointly in our hybrid model.

Fichier principal
Vignette du fichier
LLattacks_SP_AD.pdf (1) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte
licence

Dates et versions

hal-04895599 , version 1 (22-01-2025)

Licence

Identifiants

  • HAL Id : hal-04895599 , version 1

Citer

Rémi Cogranne, Marius Letourneau, Guillaume Doyen, Huu Nghia Nguyen. A Simple yet Accurate Autoadaptive Model of Network Traffic for Detection of Attacks on Low Latency Services. ACM 10th International Conference on Multimedia Systems and Signal Processing, May 2025, Fukui, Japan. ⟨hal-04895599⟩
0 Consultations
0 Téléchargements

Partager

More