Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication
Paper
Paper/Presentation Title | Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication |
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Presentation Type | Paper |
Authors | Cui, Yi (Author), Liu, Yilu (Author), Fuhr, Peter (Author) and Morales-Rodriguez, Marissa (Author) |
Journal or Proceedings Title | Proceedings of the 2018 IEEE International Symposium on Technologies for Homeland Security (HST) |
Number of Pages | 6 |
Year | 2018 |
Place of Publication | Woburn, United States |
ISBN | 9781538634431 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/THS.2018.8574151 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8574151 |
Conference/Event | 2018 IEEE International Symposium on Technologies for Homeland Security (HST) |
Event Details | 2018 IEEE International Symposium on Technologies for Homeland Security (HST) Event Date 23 to end of 24 Oct 2018 Event Location Woburn, United States |
Abstract | Electric Network Frequency (ENF) signals are the signatures of power systems that are either directly recorded from the power outlets or extracted from multimedia recordings near the electrical activities. Variations of ENF signals collected at different locations possess local environmental characteristics, which can be used as a potential fingerprint for authenticating measurements' source information. Within this paper is proposed a computational intelligence-based framework to recognize the source locations of power ENF signals within a distribution network in the US. To be more specific, a set of informative location-sensitive signatures from ENF measurements are initially extract with such measurements representative of local grid characteristics. Then these distinctive location-dependent signatures are further fed into a data mining algorithm yielding the 'source-of-origin' of ENF measurements. Experimental results using ENF data at multiple intra-grid locations have validated the proposed methodology. |
Keywords | Electric network frequency (ENF); Location-dependent signatures; Source authentication; Synchrophasor |
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 Tennessee, United States |
Oak Ridge National Laboratory, United States | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7w23/exploiting-spatial-signatures-of-power-enf-signal-for-measurement-source-authentication
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