DDoS Attacks Detection and Mitigation in 5G and Beyond Networks: A Deep Learning-based Approach - Université Gustave Eiffel Access content directly
Conference Papers Year : 2022

DDoS Attacks Detection and Mitigation in 5G and Beyond Networks: A Deep Learning-based Approach

Abstract

Network slicing, where a single physical network is partitioned into several fit-for-purpose virtual networks with different degrees of isolation and quality of service (QoS), is a key enabler of 5G and beyond mobile networks. However, it is prone to security threats such as Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose a solution based on Deep Learning (DL) that detects such attacks, and then creates a sinkhole-type slice with a small portion of physical resources to isolate and mitigate the attackers' action. Using our 5G prototype based on OpenAirInterface, we evaluate our approach by comparing several DL models in terms of detection accuracy, false positive rate, execution time, among other Machine Learning-related metrics. We also assess the performance of created 5G network slices in terms of benign/malicious users' throughput, as well as the processing time during the slicing operations. Results show that our approach is able to detect DDoS attacks in a timely manner with an accuracy of almost 97% and a false positive rate of less than 4%. We also show that our approach decreases the network throughput for the malicious users by a factor of 15, while maintaining a high network throughput for benign users.
Fichier principal
Vignette du fichier
DDoS Attacks Detection and Mitigation in 5G and Beyond Networks_A Deep Learning-based Approach.pdf (354.56 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04046661 , version 1 (26-03-2023)

Identifiers

Cite

Badre Bousalem, Vinicius Silva, Rami Langar, Sylvain Cherrier. DDoS Attacks Detection and Mitigation in 5G and Beyond Networks: A Deep Learning-based Approach. GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Dec 2022, Rio de Janeiro, Brazil. pp.1259-1264, ⟨10.1109/GLOBECOM48099.2022.10001562⟩. ⟨hal-04046661⟩
26 View
259 Download

Altmetric

Share

Gmail Facebook X LinkedIn More