Peer-to-Peer Federated Learning Based Anomaly Detection for Open Radio Access Networks

Author:

Attanayaka Dinaj1,Porambage Pawani1,Liyanage Madhusanka1,Ylianttila Mika1

Affiliation:

1. University of Oulu,Finland

Funder

European Union

Academy of Finland

Science Foundation Ireland

Publisher

IEEE

Reference21 articles.

1. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)

2. On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption

3. O-RAN: Disrupting the Virtualized RAN Ecosystem

4. O-RAN Architecture Description;O-RAN WG1 O-RAN-Architecture-Description-v02 00,2022

5. Braintorrent: A peer-to-peer environment for decentralized federated learning;roy;ArXiv Preprint,2019

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1. Secure Peer-to-Peer Federated Learning for Efficient Cyberattacks Detection in 5G and Beyond Networks;ICC 2024 - IEEE International Conference on Communications;2024-06-09

2. Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin;2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit);2024-06-03

3. A Federated Continual Learning Framework for Sustainable Network Anomaly Detection in O-RAN;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21

4. Optimizing the Energy Efficiency Using Quantum Based Load Balancing in Open Radio Access Networks;IEEE Access;2024

5. A Survey for Intrusion Detection Systems in Open RAN;IEEE Access;2024

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