Secure data sharing and analysis in cloud-based energy management systems

Anthi, Eirini, Javed, Amir ORCID: https://orcid.org/0000-0001-9761-0945, Rana, Omer F. ORCID: https://orcid.org/0000-0003-3597-2646 and Theodorakopoulos, Georgios ORCID: https://orcid.org/0000-0003-2701-7809 2017. Secure data sharing and analysis in cloud-based energy management systems. Presented at: Second EAI International Conference, IISSC 2017 and CN4IoT 2017, Brindisi, Italy, 20-21 April 2017. Published in: Longo, Antonella, Zappatore, Marco, Villari, Massimo, Rana, Omer, Bruneo, Dario, Ranjan, Rajiv, Fazin, Maria and Massonet, Philippe eds. IISSC 2017, CN4IoT 2017: Cloud Infrastructures, Services, and IoT Systems for Smart Cities. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , vol.189 Springer, pp. 228-242. 10.1007/978-3-319-67636-4_24

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Abstract

Analysing data acquired from one or more buildings (through specialist sensors, energy generation capability such as PV panels or smart meters) via a cloud-based Local Energy Management System (LEMS) is increasingly gaining in popularity. In a LEMS, various smart devices within a building are monitored and/or controlled to either investigate energy usage trends within a building, or to investigate mechanisms to reduce total energy demand. However, whenever we are connecting externally monitored/controlled smart devices there are security and privacy concerns. We describe the architecture and components of a LEMS and provide a survey of security and privacy concerns associated with data acquisition and control within a LEMS. Our scenarios specifically focus on the integration of Electric Vehicles (EV) and Energy Storage Units (ESU) at the building premises, to identify how EVs/ESUs can be used to store energy and reduce the electricity costs of the building. We review security strategies and identify potential security attacks that could be carried out on such a system, while exploring vulnerable points in the system. Additionally, we will systematically categorize each vulnerability and look at potential attacks exploiting that vulnerability for LEMS. Finally, we will evaluate current counter measures used against these attacks and suggest possible mitigation strategies.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer
ISSN:	1867-8211
Funders:	EPSRC
Date of First Compliant Deposit:	25 January 2018
Last Modified:	22 Nov 2022 08:50
URI:	https://orca.cardiff.ac.uk/id/eprint/107798

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