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

Dinaj Attanayaka; Pawani Porambage; Madhusanka Liyanage; Mika Ylianttila

doi:10.1109/ICC45041.2023.10278993

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

Dinaj Attanayaka, Pawani Porambage, Madhusanka Liyanage, Mika Ylianttila

BA6403 Network security

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

7 Citations (Scopus)

Abstract

Open radio access network (O-RAN) has been recognized as a revolutionized architecture to support the multi-class wireless services required in fifth-generation (5G) and beyond 5G networks. The openness and the distributed nature of the O-RAN architecture have created new forms of threat surfaces than the conventional RAN architecture and require complex anomaly detection mechanisms. Moreover, with the introduction of RAN intelligent controllers (RICs), it is possible to utilize advanced Artificial Intelligence (AI)/ Machine Learning (ML) algorithms based on closed control loops to detect anomalies in a data-driven manner. In this paper, we particularly investigate the use of Federated Learning (FL) for anomaly detection in the O-RAN architecture, which can further preserve data privacy. We propose a peer-to-peer (P2P) FL-based anomaly detection mechanism for the O-RAN architecture and provide a comprehensive analysis of four variants of P2P FL techniques. Moreover, we simulate the proposed models using the UNSW-NB15 dataset.

Original language	English
Title of host publication	ICC 2023 - IEEE International Conference on Communications
Editors	Michele Zorzi, Meixia Tao, Walid Saad
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	5464-5470
ISBN (Electronic)	978-1-5386-7462-8
ISBN (Print)	978-1-5386-7463-5
DOIs	https://doi.org/10.1109/ICC45041.2023.10278993
Publication status	Published - 2023
MoE publication type	A4 Article in a conference publication
Event	IEEE International Conference on Communications, ICC 2023 - Rome, Italy Duration: 28 May 2023 → 1 Jun 2023

Conference

Conference	IEEE International Conference on Communications, ICC 2023
Country/Territory	Italy
City	Rome
Period	28/05/23 → 1/06/23

Funding

This work is partly supported by VTT Technical Research Centre of Finland and by Business Finland in SUNSET6G, European Union in SPATIAL (Grant No: 101021808), Academy of Finland in 6Genesis (grant no. 318927) and Science Foundation Ireland under CONNECT phase 2 (Grant no. 13/RC/2077 P2) projects.

Keywords

5G
6G
Federated learning
Network automation
O-RAN
Privacy
RAN Intelligent controllers
Security

Access to Document

10.1109/ICC45041.2023.10278993

Cite this

Attanayaka, D., Porambage, P., Liyanage, M., & Ylianttila, M. (2023). Peer-to-Peer Federated Learning Based Anomaly Detection for Open Radio Access Networks. In M. Zorzi, M. Tao, & W. Saad (Eds.), ICC 2023 - IEEE International Conference on Communications (pp. 5464-5470). IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/ICC45041.2023.10278993

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title = "Peer-to-Peer Federated Learning Based Anomaly Detection for Open Radio Access Networks",

abstract = "Open radio access network (O-RAN) has been recognized as a revolutionized architecture to support the multi-class wireless services required in fifth-generation (5G) and beyond 5G networks. The openness and the distributed nature of the O-RAN architecture have created new forms of threat surfaces than the conventional RAN architecture and require complex anomaly detection mechanisms. Moreover, with the introduction of RAN intelligent controllers (RICs), it is possible to utilize advanced Artificial Intelligence (AI)/ Machine Learning (ML) algorithms based on closed control loops to detect anomalies in a data-driven manner. In this paper, we particularly investigate the use of Federated Learning (FL) for anomaly detection in the O-RAN architecture, which can further preserve data privacy. We propose a peer-to-peer (P2P) FL-based anomaly detection mechanism for the O-RAN architecture and provide a comprehensive analysis of four variants of P2P FL techniques. Moreover, we simulate the proposed models using the UNSW-NB15 dataset.",

keywords = "5G, 6G, Federated learning, Network automation, O-RAN, Privacy, RAN Intelligent controllers, Security",

author = "Dinaj Attanayaka and Pawani Porambage and Madhusanka Liyanage and Mika Ylianttila",

year = "2023",

doi = "10.1109/ICC45041.2023.10278993",

language = "English",

isbn = "978-1-5386-7463-5",

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editor = "Michele Zorzi and Meixia Tao and Walid Saad",

booktitle = "ICC 2023 - IEEE International Conference on Communications",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "IEEE International Conference on Communications, ICC 2023 ; Conference date: 28-05-2023 Through 01-06-2023",

}

Attanayaka, D , Porambage, P, Liyanage, M & Ylianttila, M 2023, Peer-to-Peer Federated Learning Based Anomaly Detection for Open Radio Access Networks. in M Zorzi, M Tao & W Saad (eds), ICC 2023 - IEEE International Conference on Communications. IEEE Institute of Electrical and Electronic Engineers, pp. 5464-5470, IEEE International Conference on Communications, ICC 2023, Rome, Italy, 28/05/23. https://doi.org/10.1109/ICC45041.2023.10278993

Peer-to-Peer Federated Learning Based Anomaly Detection for Open Radio Access Networks. / Attanayaka, Dinaj ; Porambage, Pawani; Liyanage, Madhusanka et al.
ICC 2023 - IEEE International Conference on Communications. ed. / Michele Zorzi; Meixia Tao; Walid Saad. IEEE Institute of Electrical and Electronic Engineers, 2023. p. 5464-5470.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Liyanage, Madhusanka

AU - Ylianttila, Mika

PY - 2023

Y1 - 2023

N2 - Open radio access network (O-RAN) has been recognized as a revolutionized architecture to support the multi-class wireless services required in fifth-generation (5G) and beyond 5G networks. The openness and the distributed nature of the O-RAN architecture have created new forms of threat surfaces than the conventional RAN architecture and require complex anomaly detection mechanisms. Moreover, with the introduction of RAN intelligent controllers (RICs), it is possible to utilize advanced Artificial Intelligence (AI)/ Machine Learning (ML) algorithms based on closed control loops to detect anomalies in a data-driven manner. In this paper, we particularly investigate the use of Federated Learning (FL) for anomaly detection in the O-RAN architecture, which can further preserve data privacy. We propose a peer-to-peer (P2P) FL-based anomaly detection mechanism for the O-RAN architecture and provide a comprehensive analysis of four variants of P2P FL techniques. Moreover, we simulate the proposed models using the UNSW-NB15 dataset.

AB - Open radio access network (O-RAN) has been recognized as a revolutionized architecture to support the multi-class wireless services required in fifth-generation (5G) and beyond 5G networks. The openness and the distributed nature of the O-RAN architecture have created new forms of threat surfaces than the conventional RAN architecture and require complex anomaly detection mechanisms. Moreover, with the introduction of RAN intelligent controllers (RICs), it is possible to utilize advanced Artificial Intelligence (AI)/ Machine Learning (ML) algorithms based on closed control loops to detect anomalies in a data-driven manner. In this paper, we particularly investigate the use of Federated Learning (FL) for anomaly detection in the O-RAN architecture, which can further preserve data privacy. We propose a peer-to-peer (P2P) FL-based anomaly detection mechanism for the O-RAN architecture and provide a comprehensive analysis of four variants of P2P FL techniques. Moreover, we simulate the proposed models using the UNSW-NB15 dataset.

KW - 5G

KW - 6G

KW - Federated learning

KW - Network automation

KW - O-RAN

KW - Privacy

KW - RAN Intelligent controllers

KW - Security

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M3 - Conference article in proceedings

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SP - 5464

EP - 5470

BT - ICC 2023 - IEEE International Conference on Communications

A2 - Zorzi, Michele

A2 - Tao, Meixia

A2 - Saad, Walid

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - IEEE International Conference on Communications, ICC 2023

Y2 - 28 May 2023 through 1 June 2023

ER -

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

Abstract

Conference

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Keywords

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SUNSET-6G : Sustainable Network Security Tech for 6G

Cite this

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

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Projects

SUNSET-6G : Sustainable Network Security Tech for 6G

Cite this