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, 2019 http://doi.acm.org/10.1145/3307772.3330154
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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Identifying Security Challenges in Renewable Energy Systems
T2 - Tenth ACM International Conference on Future Energy Systems (ACM e-Energy)
AU - Jindal, Anish
AU - Marnerides, Angelos
AU - Scott, Andrew
AU - Hutchison, David
N1 - Conference code: 10
PY - 2019/6/28
Y1 - 2019/6/28
N2 - Distributed renewable energy systems (DRESs) and their interconnection network, typically using Internet-based protocols, are susceptible to a wide range of cybersecurity and resilience challenges. These challenges have been shown to cause problems for the overall grid optimization process. In order to detect such events, we argue that an adequate correlation between network and energy generation data is required. Therefore, in this study, we provide a work-in-progress insight related to the profiling of real network data and energy generation measurements gathered by a local windturbine at Lancaster University. We argue that such an analysis is very important to profile various attack vectors in the modern energy networks that consider DRESs and take necessary actions to prevent any data breaches in the future.
AB - Distributed renewable energy systems (DRESs) and their interconnection network, typically using Internet-based protocols, are susceptible to a wide range of cybersecurity and resilience challenges. These challenges have been shown to cause problems for the overall grid optimization process. In order to detect such events, we argue that an adequate correlation between network and energy generation data is required. Therefore, in this study, we provide a work-in-progress insight related to the profiling of real network data and energy generation measurements gathered by a local windturbine at Lancaster University. We argue that such an analysis is very important to profile various attack vectors in the modern energy networks that consider DRESs and take necessary actions to prevent any data breaches in the future.
U2 - 10.1145/3307772.3330154
DO - 10.1145/3307772.3330154
M3 - Conference contribution/Paper
SN - 9781450366717
SP - 370
EP - 372
BT - e-Energy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems
PB - ACM
CY - New York
Y2 - 25 June 2019 through 28 June 2019
ER -