Practical challenges of attack detection in microgrids using machine learning
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MDPI
Abstract
The move towards renewable energy and technological advancements in the generation, distribution
and transmission of electricity have increased the popularity of microgrids. The popularity
of these decentralised applications has coincided with advancements in the field of telecommunications
allowing for the efficient implementation of these applications. This convenience has, however,
also coincided with an increase in the attack surface of these systems, resulting in an increase in
the number of cyber-attacks against them. Preventative network security mechanisms alone are
not enough to protect these systems as a critical design feature is system resilience, so intrusion
detection and prevention system are required. The practical consideration for the implementation of
the proposed schemes in practice is, however, neglected in the literature. This paper attempts to address
this by generalising these considerations and using the lessons learned from water distribution
systems as a case study. It was found that the considerations are similar irrespective of the application
environment even though context-specific information is a requirement for effective deployment.
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Keywords
Microgrids, Cyber–physical systems, Industrial control systems, Intrusion detection systems, Machine learning, Network security, SDG-09: Industry, innovation and infrastructure, SDG-07: Affordable and clean energy
Sustainable Development Goals
SDG-07:Affordable and clean energy
SDG-09: Industry, innovation and infrastructure
SDG-09: Industry, innovation and infrastructure
Citation
Ramotsoela, D.T.; Hancke, G.P.; Abu-Mahfouz, A.M. Practical Challenges of Attack Detection in Microgrids Using Machine Learning. Journal of Sensor and Actuator Networks. 2023, 12, 7. https://doi.org/10.3390/jsan12010007.