Adversarial Machine Learning in Smart Energy Systems

Bor, Martin and Marnerides, Angelos and Molineux, Andy and Wattam, Steve and Roedig, Utz (2019) Adversarial Machine Learning in Smart Energy Systems. In: e-Energy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems. ACM, USA, pp. 413-415. ISBN 9781450366717

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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.

Item Type: Contribution in Book/Report/Proceedings
Additional Information: © 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
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 134248
Deposited By: ep_importer_pure
Deposited On: 22 Jun 2019 01:00
Refereed?: Yes
Published?: Published
Last Modified: 21 Feb 2020 06:00

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