TY - GEN
T1 - Comparison of various interpolation techniques to infer localization of audio files using ENF signals
AU - Han, Hyekyung
AU - Lee, Kang Hoon
AU - Jeon, Youngbae
AU - Yoon, Ji Won
N1 - Funding Information:
This work was supported and funded by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD).
Publisher Copyright:
© 2020 IEEE
PY - 2020
Y1 - 2020
N2 - Electrical Network Frequency (ENF) is a frequency of the electrical power signal of the power grid that plays a key role in the level of security. There is a difference in the values on the supply and demand on power usage. Due to its distinctive value, the ENF data hold great importance in the field of security. Examining the ENF signal makes it possible to trace the location where the ENF signal was generated. By making the most use of certain interpolation techniques, we can estimate the ENF value of a specific location and evaluate the estimated performance. Interpolating the ENF signals on the target location can increase the accuracy of the estimate for the unacquainted ENF signals. In this paper, we interpolated the ENF values of the power grid of the United States by using three different methods: IDW, Ordinary Kriging, and Universal Kriging. Then we evaluated the RMSE calculated by varying the hyper-parameters and models of interpolation methods. As a result, it was found that applying the Ordinary Kriging in the Western grid had the lowest RMSE. For the Eastern power grid, it was the IDW with λ = -1 which showed the lowest RMSE. We concluded that each power grid had different characteristics. Therefore different interpolation techniques should be applied to each case for precise approximation.
AB - Electrical Network Frequency (ENF) is a frequency of the electrical power signal of the power grid that plays a key role in the level of security. There is a difference in the values on the supply and demand on power usage. Due to its distinctive value, the ENF data hold great importance in the field of security. Examining the ENF signal makes it possible to trace the location where the ENF signal was generated. By making the most use of certain interpolation techniques, we can estimate the ENF value of a specific location and evaluate the estimated performance. Interpolating the ENF signals on the target location can increase the accuracy of the estimate for the unacquainted ENF signals. In this paper, we interpolated the ENF values of the power grid of the United States by using three different methods: IDW, Ordinary Kriging, and Universal Kriging. Then we evaluated the RMSE calculated by varying the hyper-parameters and models of interpolation methods. As a result, it was found that applying the Ordinary Kriging in the Western grid had the lowest RMSE. For the Eastern power grid, it was the IDW with λ = -1 which showed the lowest RMSE. We concluded that each power grid had different characteristics. Therefore different interpolation techniques should be applied to each case for precise approximation.
KW - Electrical network frequency
KW - Interpolation
KW - Inverse distance weight
KW - Kriging
KW - Location identification
KW - Ordinary kriging
KW - Universal kriging
UR - http://www.scopus.com/inward/record.url?scp=85124877482&partnerID=8YFLogxK
U2 - 10.1109/ICSSA51305.2020.00015
DO - 10.1109/ICSSA51305.2020.00015
M3 - Conference contribution
AN - SCOPUS:85124877482
T3 - Proceedings - 2020 International Conference on Software Security and Assurance, ICSSA 2020
SP - 46
EP - 51
BT - Proceedings - 2020 International Conference on Software Security and Assurance, ICSSA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Software Security and Assurance, ICSSA 2020
Y2 - 28 October 2020 through 30 October 2020
ER -