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  • Adversarial_Machine_Learning_in_Smart_Energy_Systems

    Rights statement: © ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in e-Energy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems http://doi.acm.org/10.1145/3307772.3330171

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Adversarial Machine Learning in Smart Energy Systems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
Publication date25/06/2019
Host publicatione-Energy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems
Place of PublicationNew York
PublisherACM
Pages413-415
Number of pages3
ISBN (Electronic)9781450366717
Original languageEnglish
EventTenth ACM International Conference on Future Energy Systems (ACM e-Energy) - Phoenix, AZ, United States
Duration: 25/06/201928/06/2019
Conference number: 10
https://energy.acm.org/conferences/eenergy/2019/

Conference

ConferenceTenth ACM International Conference on Future Energy Systems (ACM e-Energy)
Abbreviated titleACM e-Energy 2019
CountryUnited States
CityPhoenix, AZ
Period25/06/1928/06/19
Internet address

Conference

ConferenceTenth ACM International Conference on Future Energy Systems (ACM e-Energy)
Abbreviated titleACM e-Energy 2019
CountryUnited States
CityPhoenix, AZ
Period25/06/1928/06/19
Internet address

Abstract

Smart Energy Systems represent a radical shift in the approach to energy generation and demand, driven by decentralisation of the energy system to large numbers of low-capacity devices. Managing this flexibility is often driven by machine learning, and requires real-time control and aggregation of these devices, involving a diverse set of companies and devices and creating a longer chain of trust. This poses a security risk, as it is sensitive to adversarial machine learning, whereby models are fooled through malicious input, either for financial gain or to cause system disruption. We show the feasibility of such an attack by analysing empirical data of a real system, and propose directions for future research related to detection and defence mechanisms for these kind of attacks.

Bibliographic note

© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in e-Energy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems http://doi.acm.org/10.1145/3307772.3330171