S. Jayasinghe, Y. Siriwardhana, P. Porambage, M. Liyanage and M. Ylianttila, "Federated Learning based Anomaly Detection as an Enabler for Securing Network and Service Management Automation in Beyond 5G Networks," 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Grenoble, France, 2022, pp. 345-350, doi: 10.1109/EuCNC/6GSummit54941.2022.9815754.
Federated learning based anomaly detection as an enabler for securing network and service management automation in beyond 5G networks
|Author:||Jayasinghe, Suwani1; Siriwardhana, Yushan1; Porambage, Pawani1;|
1Centre for Wireless Communications, University of Oulu, Finland
2School of Computer Science, University College Dublin, Ireland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202301245347
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-01-24
Network automation is a necessity in order to meet the unprecedented demand in the future networks and zero touch network architecture is proposed to cater such requirements. Closed-loop and artificial intelligence are key enablers in this proposed architecture in critical elements such as security. Apart from the arising privacy concerns, machine learning models can also face resource limitations. Federated learning is a machine learning-based technique that addresses both privacy and communication efficiency issues. Therefore, we propose a federated learning-based model incorporating the ZSM architecture for network automation. The paper also contains the simulations and results of the proposed multi-stage federated learning model that uses the UNSW-NB15 dataset.
European Conference on Networks and Communications
|Pages:||345 - 350|
2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 07-10 June 2022, Grenoble, France
Joint European Conference on Networks and Communications & 6G Summit
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is supported by 6Genesis Flagship (grant 318927) project. The research leading to these results partly received funding from European Union’s Horizon 2020 research and innovation programme under grant agreement no 871808 (5G PPP project INSPIRE-5Gplus) and 101021808 (H2020 SPATIAL project). The paper reflects only the authors’ views. The Commission is not responsible for any use that may be made of the information it contains.
|EU Grant Number:||
(871808) INSPIRE-5Gplus - INtelligent Security and PervasIve tRust for 5G and Beyond
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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