Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication

Yi Cui; Yilu Liu; Peter Fuhr; Marissa Morales-Rodriguez

doi:10.1109/THS.2018.8574151

Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication

Yi Cui, Yilu Liu, Peter Fuhr, Marissa Morales-Rodriguez

Grid Communications and Security

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

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.

Original language	English
Title of host publication	2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538634431
DOIs	https://doi.org/10.1109/THS.2018.8574151
State	Published - Dec 12 2018
Event	2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018 - Woburn, United States Duration: Oct 23 2018 → Oct 24 2018

Publication series

Name	2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018

Conference

Conference	2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018
Country/Territory	United States
City	Woburn
Period	10/23/18 → 10/24/18

Funding

This work was supported primarily by the Engineering Research Centre Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program. This work was supported primarily by the Engineering Research Centre Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program

Funders	Funder number
National Science Foundation	EEC-1041877
U.S. Department of Energy
Norsk Sykepleierforbund
National Science Foundation

Keywords

Electric network frequency (ENF)
Location-dependent signatures
Source authentication
Synchrophasor

Access to Document

10.1109/THS.2018.8574151

Cite this

Cui, Y., Liu, Y., Fuhr, P., & Morales-Rodriguez, M. (2018). Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication. In 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018 Article 8574151 (2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/THS.2018.8574151

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title = "Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication",

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.",

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author = "Yi Cui and Yilu Liu and Peter Fuhr and Marissa Morales-Rodriguez",

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Cui, Y, Liu, Y, Fuhr, P & Morales-Rodriguez, M 2018, Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication. in 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018., 8574151, 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018, Woburn, United States, 10/23/18. https://doi.org/10.1109/THS.2018.8574151

Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication. / Cui, Yi; Liu, Yilu; Fuhr, Peter et al.
2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8574151 (2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Morales-Rodriguez, Marissa

PY - 2018/12/12

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N2 - 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.

AB - 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.

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Cui Y, Liu Y, Fuhr P, Morales-Rodriguez M. Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication. In 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8574151. (2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018). doi: 10.1109/THS.2018.8574151

Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication

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