Federated Learning based Anomaly Detection as an Enabler for Securing Network and Service Management Automation in Beyond 5G Networks
Suwani Jayasinghe, Yushan Siriwardhana, Pawani Porambage, Madhusanka Liyanage, Mika Ylianttila
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 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.