Inherent vulnerability of demand response optimisation against false data injection attacks in smart grids

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Abstract

The transition of energy networks to so-called smart grids benefits from advancements in Internet of Things technology. Energy management systems enable efficient and effective demand response (DR) schemes optimising load distribution. The increased user involvements through such DR schemes creates a new vector for false data injection attacks (FDIA), where authentic users themselves inject false data. Unlike in most existing FDIAs, no breaches to communication or devices are needed to execute this type of FDIA. In this work, we depict that this new FDIA can impact any optimisation-based DR scheme. Further, we show that adversaries achieve financial benefits independently from the actual algorithm used for optimisation, as long as they are able to inject false demand predictions. Compared to traditional FDIAs, reliable security mechanisms such as proper authentication, security protocols, security controls or sealed/controlled devices cannot prevent this new type of FDIA. Additionally, we show that there is no straightforward solution and we highlight the need for highly reliable FDIA detection mechanisms to thwart this type of attacks.

Original languageEnglish
Title of host publicationProceedings of IEEE/IFIP Network Operations and Management Symposium 2020
EditorsImen Grida Ben Yahia, Alex Galis, Istvan Godor
Place of PublicationNew York NY USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages9
ISBN (Electronic)9781728149738
ISBN (Print)9781728149745
DOIs
Publication statusPublished - Apr 2020
EventIEEE Network Operations and Management Symposium 2020 - Budapest, Hungary
Duration: 20 Apr 202024 Apr 2020
https://ieeexplore-ieee-org.ezproxy.lib.monash.edu.au/xpl/conhome/9107308/proceeding (Proceedings)
https://noms2020.ieee-noms.org (Website)

Publication series

NameIEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1542-1201
ISSN (Electronic)2374-9709

Conference

ConferenceIEEE Network Operations and Management Symposium 2020
Abbreviated titleNOMS 2020
Country/TerritoryHungary
CityBudapest
Period20/04/2024/04/20
Internet address

Keywords

  • Demand response
  • False data injection attack
  • Inherent vulnerabilities
  • Smart grids

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