Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Power Grid Estimation Using Electric Network Frequency Signals

Authors
Bang, WoorimYoon, Ji Won
Issue Date
24-9월-2019
Publisher
WILEY-HINDAWI
Citation
SECURITY AND COMMUNICATION NETWORKS, v.2019
Indexed
SCIE
SCOPUS
Journal Title
SECURITY AND COMMUNICATION NETWORKS
Volume
2019
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/62840
DOI
10.1155/2019/1982168
ISSN
1939-0114
Abstract
The electric network frequency (ENF) has a statistical uniqueness according to time and location. The ENF signal is always slightly fluctuating for the load balance of the power grid around the fundamental frequency. The ENF signals can be obtained from the power line using a frequency disturbance recorder (FDR). The ENF signal can also be extracted from video files or audio files because the ENF signal is also saved due to the influence of the electromagnetic field when video files or audio files are recorded. In this paper, we propose a method to find power grid from ENF signals collected from various time and area. We analyzed ENF signals from the distribution level of the power system and online uploaded video files. Moreover, a hybrid feature extraction approach, which employs several features, is proposed to infer the location of the signal belongs regardless of the time that the signal was collected. Employing our suggested feature extraction methods, the signal which extracted from the power line can be classified 95.21% and 99.07% correctly when ENF signals have 480 and 1920 data points, respectively. In the case of ENF signals extracted from multimedia, the accuracy varies greatly according to the recorded environment such as network status and microphone quality. When constructing a feature vector from 120 data points of ENF signals, we could identify the power grid had an average of 94.17% accuracy from multimedia.
Files in This Item
There are no files associated with this item.
Appears in
Collections
School of Cyber Security > Department of Information Security > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Yoon, Ji Won photo

Yoon, Ji Won
정보보호학과
Read more

Altmetrics

Total Views & Downloads

BROWSE