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Federated Learning based Anomaly Detection as an Enabler for Securing Network and Service Management Automation in Beyond 5G Networks
Date Issued
2022-06-10
Date Available
2022-08-24T11:10:24Z
Abstract
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 com- munication 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.
Sponsorship
European Commission Horizon 2020
Type of Material
Conference Publication
Publisher
IEEE
Start Page
345
End Page
350
Copyright (Published Version)
2022 IEEE
Language
English
Status of Item
Peer reviewed
Part of
2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
This item is made available under a Creative Commons License
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