Federated Learning based Anomaly Detection as an Enabler for Securing Network and Service Management Automation in Beyond 5G Networks

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Title: Federated Learning based Anomaly Detection as an Enabler for Securing Network and Service Management Automation in Beyond 5G Networks
Authors: Jayasinghe, SuwaniSiriwardhana, YushanPorambage, PawaniLiyanage, MadhusankaYlianttila, Mika
Permanent link: http://hdl.handle.net/10197/13092
Date: 10-Jun-2022
Online since: 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.
Funding Details: European Commission Horizon 2020
Type of material: Conference Publication
Publisher: IEEE
Start page: 345
End page: 350
Copyright (published version): 2022 IEEE
Keywords: 5GBeyond 5GNetwork automationSecurityFederated learningZSM
DOI: 10.1109/eucnc/6gsummit54941.2022.9815754
Language: en
Status of Item: Peer reviewed
Is 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: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection

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