Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication

Conference · Mon Oct 01 00:00:00 EDT 2018

DOI:https://doi.org/10.1109/THS.2018.8574151· OSTI ID:1558521

CUI, YI ^[1]; Liu, Yilu ^[2]; Fuhr, Peter L.  ^[2]; Morales Rodriguez, Marissa E. ^[2]

---

1. The University of Tennessee, Knoxville
2. ORNL

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.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1558521
• Resource Relation: Conference: 2018 IEEE International Symposium on Technologies for Homeland Security (HST 2018) - Woburn, Massachusetts, United States of America - 10/23/2018 8:00:00 AM-10/24/2018 8:00:00 AM
• Country of Publication: United States
• Language: English