Adversarial Machine Learning in Smart Energy Systems

Bor, M. C., Marnerides, A. K. , Molineux, A., Wattam, S. and Roedig, U. (2019) Adversarial Machine Learning in Smart Energy Systems. e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems, Phoenix AZ, USA, June 2019. (doi: 10.1145/3307772.3330171)

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.

• Item Type: Conference or Workshop Item
• Additional Information: Poster published in e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems Adversarial Machine Learning in Smart Energy Systems. Pages 413–415.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Marnerides, Dr Angelos
• Authors: Bor, M. C., Marnerides, A. K., Molineux, A., Wattam, S., and Roedig, U.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: e-Energy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems

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