Authors:
Svetlana Boudko
1
;
Habtamu Abie
1
;
Ethiopia Nigussie
2
and
Reijo Savola
3
Affiliations:
1
ICT Research, Norwegian Computing Center, P.O. Box 114 Blindern, Oslo, Norway
;
2
Department of Future Technologies, University of Turku, Turku, Finland
;
3
Security Assurance, VTT Technical Research Centre of Finland Ltd., Oulu, Finland
Keyword(s):
Multi-microgrid, Machine Learning, Federated Learning, Cybersecurity, Collaborative Protocols, Adaptive Mechanisms.
Abstract:
Multi-microgrids (MMGs) provide economic and environmental benefits to society by improving operational flexibility, stability and reliability of a smart grid. MMGs have greater complexity than conventional power networks due to the use of multiple infrastructures, communication protocols, controllers, and intelligent electronic devices. The distributed and heterogeneous connectivity technologies of the MMGs and their need to exchange information with external sources as well as the vulnerabilities in the communication networks and software-based components, make MMGs susceptible to cyberattacks. In this work, we present a conceptual framework for collaborative adaptive cybersecurity that is able to proactively detect security incidents. The framework utilizes federated learning for collaborative training of shared prediction models in a decentralized manner. The methodology used in this research is mainly analytical. This involves analysis of how the principles of a collaborative ad
aptive cybersecurity can be applied to the MMG environments resulting in the development of theoretical models which can then be validated in practice by prototyping and using real time simulation.
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