Combining NWDAF and ML for Abnormal Traffic Detection in Beyond 5G
OAI Webinar Series Chapter 9
Abstract: In this webinar, we will explore the microservices-based architecture of OAI NWDAF in the 5G Core Network. We will focus on integrating 3GPP applications as microservices to enhance flexibility and scalability. We will showcase the implementation of an ML-based NWDAF service for real-world abnormal traffic detection. Demonstrating the integration with OAI 5G CN and RAN, we will highlight NWDAF’s capability in collecting and analyzing data from a live 5G network, effectively detecting abnormal traffic generated by a real UE. Join us to witness these practical applications of NWDAF in the 5G network.
This webinar was presented by:
Abdelkader Mekrache
Abdelkader is a PhD candidate at EURECOM’s Communication Systems Department. His primary focus is on advanced Network Management frameworks in next-generation wireless networks under the supervision of Prof. Adlen Ksentini. He is an active participant in collaborative research and notably contributes to the OpenAirInterface project. His diverse research interests include Next-Generation Networking, 5G Core Networks, Network Management and orchestration, Artificial Intelligence, and Reasoning Algorithms for 5G and beyond.
Karim Boutiba
Karim is a PhD candidate and research assistant at the Communication Systems Department of EURECOM. He is working toward enforcing Network Slicing in the next-generation wireless networks under the supervision of Prof Adlen Ksentini. He was involved in collaborative research projects and an active contributor to the OpenAirInterface project. His research interests include Next-Generation Networking, 5G New Radio (NR), Network Slicing, Open RAN, Optimization algorithms, and Reinforcement Learning for 5G networks and beyond.
