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Cyber-Physical-Security Framework for Building Energy Management System
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-4210-8672
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2016 (English)In: 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems, ICCPS 2016 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2016, 7479072Conference paper, Published paper (Refereed)
Abstract [en]

Energy management systems (EMS) are used to control energy usage in buildings and campuses, by employing technologies such as supervisory control and data acquisition (SCADA) and building management systems (BMS), in order to provide reliable energy supply and maximise user comfort while minimising energy usage. Historically, EMS systems were installed when potential security threats were only physical. Nowadays, EMS systems are connected to the building network and as a result directly to the outside world. This extends the attack surface to potential sophisticated cyber-attacks, which adversely impact EMS operation, resulting in service interruption and downstream financial implications. Currently, the security systems that detect attacks operate independently to those which deploy resiliency policies and use very basic methods. We propose a novel EMS cyber-physical-security framework that executes a resilient policy whenever an attack is detected using security analytics. In this framework, both the resilient policy and the security analytics are driven by EMS data, where the physical correlations between the data-points are identified to detect outliers and then the control loop is closed using an estimated value in place of the outlier. The framework has been tested using a reduced order model of a real EMS site.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. 7479072
Keyword [en]
Cyber-physical-security, energy management system, resilient control, virtual sensor, security analytics
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-187756DOI: 10.1109/ICCPS.2016.7479072ISI: 000386348200006Scopus ID: 2-s2.0-84979057777ISBN: 978-150901772-0 (print)OAI: oai:DiVA.org:kth-187756DiVA: diva2:931549
Conference
7th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2016, Vienna, Austria, 11 April 2016 through 14 April 2016
Note

QC 20161031

Available from: 2016-05-29 Created: 2016-05-29 Last updated: 2016-11-21Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
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More languages
Output format
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