5G and beyond; Deep learning; Federated learning; IDS; NON-IID; 5g and beyond; Centralised; Collaborative approach; Data distribution; Intrusion Detection Systems; Learning models; NON-identically distributed; Service provider; Computer Science (all); Law; General Computer Science
Abstract :
[en] 5G's service providers now leverage Deep Learning (DL) to automate their network slice management, provisioning, and security. To this end, each slice owner contributes data to feed a common dataset used to train centralized learning models. However, this method raises privacy considerations that prevent its usage. Therefore, Federated learning (FL), a collaborative approach that ensures data privacy, is being investigated while striving toward the same performance as centralized learning. As 5G and beyond services are so diverse, the local slice's data is not intended to reflect the entire data distribution. Thus, local data of slices are Non-Independently and non-Identically distributed (Non-IID), posing a challenge for FL-based models. In this paper, we investigate the use of FL to secure network slices and detect potential attacks. For that purpose, we first propose an architecture for deploying intrusion detection systems (IDSs) in 5G and beyond networks. Next, we thoroughly evaluate the latest state-of-art FL algorithms, including FedAvg, FedProx, FedPer, and SCAFFOLD, in the context of Independently and Identically Distributed (IID) and Non-IID data distributions. We compare these FL models to centralized and local DL models. We find that SCAFFOLD outperforms all the other FL algorithms and ensures a stable learning loss convergence, a promising finding that strengthens the case for leveraging FL in IDS development. Nevertheless, none of the FL models could achieve the centralized model's performance in Non-IID scenarios.
Disciplines :
Computer science
Author, co-author :
Djaidja, Taki Eddine Toufik ; DRIVE Laboratory EA1859, University of Burgundy, Nevers, France
Brik, Bouziane ; Computer Science Department, College of Computing and Informatics, Sharjah University, United Arab Emirates
BOUALOUACHE, Abdelwahab ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Senouci, Sidi Mohammed; DRIVE Laboratory EA1859, University of Burgundy, Nevers, France
Ghamri-Doudane, Yacine; L3I Laboratory, Univ. La Rochelle, France
External co-authors :
yes
Language :
English
Title :
Federated learning for 5G and beyond, a blessing and a curse- an experimental study on intrusion detection systems
FNR14891397 - Intelligent Orchestrated Security And Privacy-aware Slicing For 5g And Beyond Vehicular Networks, 2020 (01/04/2021-31/03/2024) - Thomas Engel
Funders :
Agence Nationale de la Recherche Fonds National de la Recherche Luxembourg
Funding text :
This work was supported by the 5G-INSIGHT bilateral project (ID: 14891397 ) / ( ANR-20-CE25-0015-16 ), funded by the Luxembourg National Research Fund (FNR), and by the French National Research Agency (ANR).
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