Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning

Abstract The surging usage of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS) with millisecond range latency and massive machine to machine communications where 5G could act. However, 5G suffers from inherent protocols, hardware, and softw...

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Autores principales:	Manoj Basnet, Mohd. Hasan Ali
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
Acceso en línea:	https://doaj.org/article/9d759d3528134a3f87c18e847b692a24
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spelling	oai:doaj.org-article:9d759d3528134a3f87c18e847b692a242021-11-16T15:47:59ZExploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning1751-86951751-868710.1049/gtd2.12275https://doaj.org/article/9d759d3528134a3f87c18e847b692a242021-12-01T00:00:00Zhttps://doi.org/10.1049/gtd2.12275https://doaj.org/toc/1751-8687https://doaj.org/toc/1751-8695Abstract The surging usage of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS) with millisecond range latency and massive machine to machine communications where 5G could act. However, 5G suffers from inherent protocols, hardware, and software vulnerabilities that seriously threaten the communicating entities' cyber‐physical security. To overcome these limitations in the EVCS system, this paper analyses the impact of False Data Injection (FDI) and Distributed Denial of Services (DDoS) attacks on the operation of EVCS. This work is an extension of the previously published conference paper about the EVCS. As new features, this paper simulates the FDI attack and the syn flood DDoS attacks on 5G enabled remote Supervisory Control and Data Acquisition (SCADA) system that controls the solar photovoltaics (PV) controller, Battery Energy Storage (BES) controller, and EV controller of the EVCS. The extent of delay has been increased to more than 500 ms with the severe DDoS attack via 5G. The attacks make the EVCS system oscillate or shift the DC operating point. The frequency of oscillation, its damping, and the system's resiliency are found to be related to the attacks' intensity and the target controller. Finally, the novel stacked Long Short‐Term Memory (LSTM) based intrusion detection systems (IDS) are proposed solely based on the electrical fingerprint. This model can detect the stealthy cyberattacks that bypass the cyber layer and go unnoticed in the monitoring system with nearly 100% detection accuracy.Manoj BasnetMohd. Hasan AliWileyarticleDistribution or transmission of electric powerTK3001-3521Production of electric energy or power. Powerplants. Central stationsTK1001-1841ENIET Generation, Transmission & Distribution, Vol 15, Iss 24, Pp 3435-3449 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
spellingShingle	Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841 Manoj Basnet Mohd. Hasan Ali Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning
description	Abstract The surging usage of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS) with millisecond range latency and massive machine to machine communications where 5G could act. However, 5G suffers from inherent protocols, hardware, and software vulnerabilities that seriously threaten the communicating entities' cyber‐physical security. To overcome these limitations in the EVCS system, this paper analyses the impact of False Data Injection (FDI) and Distributed Denial of Services (DDoS) attacks on the operation of EVCS. This work is an extension of the previously published conference paper about the EVCS. As new features, this paper simulates the FDI attack and the syn flood DDoS attacks on 5G enabled remote Supervisory Control and Data Acquisition (SCADA) system that controls the solar photovoltaics (PV) controller, Battery Energy Storage (BES) controller, and EV controller of the EVCS. The extent of delay has been increased to more than 500 ms with the severe DDoS attack via 5G. The attacks make the EVCS system oscillate or shift the DC operating point. The frequency of oscillation, its damping, and the system's resiliency are found to be related to the attacks' intensity and the target controller. Finally, the novel stacked Long Short‐Term Memory (LSTM) based intrusion detection systems (IDS) are proposed solely based on the electrical fingerprint. This model can detect the stealthy cyberattacks that bypass the cyber layer and go unnoticed in the monitoring system with nearly 100% detection accuracy.
format	article
author	Manoj Basnet Mohd. Hasan Ali
author_facet	Manoj Basnet Mohd. Hasan Ali
author_sort	Manoj Basnet
title	Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning
title_short	Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning
title_full	Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning
title_fullStr	Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning
title_full_unstemmed	Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning
title_sort	exploring cybersecurity issues in 5g enabled electric vehicle charging station with deep learning
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/9d759d3528134a3f87c18e847b692a24
work_keys_str_mv	AT manojbasnet exploringcybersecurityissuesin5genabledelectricvehiclechargingstationwithdeeplearning AT mohdhasanali exploringcybersecurityissuesin5genabledelectricvehiclechargingstationwithdeeplearning
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Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning

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