Towards Federated Learning-based Collaborative Adaptive Cybersecurity for Multi-microgrids

Svetlana Boudko, Habtamu Abie, Ethiopia Nigussie, Reijo Savola

2021

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 adaptive 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.

Paper Citation

in Harvard Style

Boudko S., Abie H., Nigussie E. and Savola R. (2021). Towards Federated Learning-based Collaborative Adaptive Cybersecurity for Multi-microgrids. In Proceedings of the 18th International Conference on Wireless Networks and Mobile Systems - Volume 1: WINSYS, ISBN 978-989-758-529-6, pages 83-90. DOI: 10.5220/0010580700830090

in Bibtex Style

@conference{winsys21,
author={Svetlana Boudko and Habtamu Abie and Ethiopia Nigussie and Reijo Savola},
title={Towards Federated Learning-based Collaborative Adaptive Cybersecurity for Multi-microgrids},
booktitle={Proceedings of the 18th International Conference on Wireless Networks and Mobile Systems - Volume 1: WINSYS,},
year={2021},
pages={83-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010580700830090},
isbn={978-989-758-529-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Wireless Networks and Mobile Systems - Volume 1: WINSYS,
TI - Towards Federated Learning-based Collaborative Adaptive Cybersecurity for Multi-microgrids
SN - 978-989-758-529-6
AU - Boudko S.
AU - Abie H.
AU - Nigussie E.
AU - Savola R.
PY - 2021
SP - 83
EP - 90
DO - 10.5220/0010580700830090