Deep Learning-based Approach for DDoS Attacks Detection and Mitigation in 5G and Beyond Mobile Networks

Badre Bousalem (1) , Vinicius F Silva (1) , Rami Langar (1) , Sylvain Cherrier (1)

1 LIGM - Laboratoire d'Informatique Gaspard-Monge

In this demo, we present a 5G prototype for attacks detection and mitigation in sliced networks leveraging Machine Learning (ML). Our prototype, based on OpenAirInterface, allows creating network slices on demand and managing physical resources dynamically according to the users' behavior, while considering the inputs from a northbound Software Defined Network (SDN) application. We focus here on Distributed Denial of Service (DDoS) attacks, where one or multiple malicious users generate attacks on the 5G Core Network. Based on our developed ML module, we show that our prototype is able to detect such attacks, then automatically creates a sinkhole-type slice with a small portion of physical resources, and isolates the malicious users within this slice to mitigate the attackers' action. We demonstrate the effectiveness of our approach by showing the decrease in the network throughput for the malicious users by a factor of 15, while maintaining a high network throughput for benign users.

Mots clés: 5G; Slicing; Cybersecurity; Deep Learning

Domaines: Réseaux et télécommunications [cs.NI]; Cryptographie et sécurité [cs.CR]; Apprentissage [cs.LG]

Badre Bousalem, Vinicius F Silva, Rami Langar, Sylvain Cherrier. Deep Learning-based Approach for DDoS Attacks Detection and Mitigation in 5G and Beyond Mobile Networks. 8th International Conference on Network Softwarization (NetSoft 2022), Jun 2022, Milan, Italy. pp.228-230, ⟨10.1109/NetSoft54395.2022.9844053⟩. ⟨hal-04046662⟩