Multifractal Characterization of Distribution Synchrophasors for Cybersecurity Defense of Smart Grids
Article
Article Title | Multifractal Characterization of Distribution Synchrophasors for Cybersecurity Defense of Smart Grids |
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ERA Journal ID | 123777 |
Article Category | Article |
Authors | Cui, Yi (Author), Bai, Feifei (Author), Yan, Ruifeng (Author), Saha, Tapan (Author), Mosadeghy, Mehdi (Author), Yin, Hongzhi (Author), Ko, Ryan K. L. (Author) and Liu, Yilu (Author) |
Journal Title | IEEE Transactions on Smart Grid |
Journal Citation | 13 (2), pp. 1658-1661 |
Number of Pages | 4 |
Year | 2022 |
Place of Publication | United States |
ISSN | 1949-3053 |
1949-3061 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/tsg.2021.3132536 |
Web Address (URL) | https://ieeexplore.ieee.org/document/9634105 |
Abstract | 'Source ID Mix' spoofing emerged as a new type of cyber-attack on Distribution Synchrophasors (DS) where adversaries have the capability to swap the source information of DS without changing the measurement values. Accurate detection of such a highly-deceptive attack is a challenging task especially when the spoofing attack happens on short fragments of DS recorded within a relatively small geographical scale. This letter proposes an effective approach to detect this cyber-attack by realizing the multifractal characteristics of DS measurements. First, the multifractal cross-correlation of DS measured at multiple intra-state locations is revealed. Then the derived correlation is integrated with weighted two-dimensional multifractal surface interpolation to reconstruct quasi high-resolution signals. Finally, informative location-specific signatures are extracted from the high-resolution DS and they are integrated with advanced machine learning techniques for source authentication. Experiments using the real-life DS are performed to verify the proposed method. |
Keywords | cyber-physical security; distribution network; OT security; phasor measurement unit (PMU); Source ID Mix |
ANZSRC Field of Research 2020 | 460403. Data security and protection |
400803. Electrical energy generation (incl. renewables, excl. photovoltaics) | |
460308. Pattern recognition | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Queensland |
NOJA Power, Australia | |
University of Tennessee, United States | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7vzq/multifractal-characterization-of-distribution-synchrophasors-for-cybersecurity-defense-of-smart-grids
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