Model-Free Data Authentication for Cyber Security in Power Systems

• Published in: IEEE transactions on smart grid Vol. 11; no. 5; pp. 4565 - 4568
• Main Authors: Liu, Shengyuan, You, Shutang, Yin, He, Lin, Zhenzhi, Liu, Yilu, Yao, Wenxuan, Sundaresh, Lakshmi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-09-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Authentication; back propagation (BP) network; Cyber security; Cybersecurity; data authentication; Data mining; ensemble empirical mode decomposition (EEMD); fast Fourier transform (FFT); Fast Fourier transformations; Feature extraction; Fourier transforms; Frequency measurement; Machine learning; Neural networks; Power measurement; Power systems; POWER TRANSMISSION AND DISTRIBUTION; Situational awareness; Spoofing; Training
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2020.2986704

With the development and wide deployment of measurement equipment, data can be automatically measured and visualized for situation awareness in power systems. However, the cyber security of power systems is also threated by data spoofing attacks. This letter proposed a measurement data source authentication (MDSA) algorithm based on feature extraction techniques including ensemble empirical mode decomposition (EEMD) and fast Fourier transform (FFT), and machine learning for real-time measurement data classification. Compared with previous work, the proposed algorithm can achieve higher accuracy of MDSA using a shorter window of data from closely located synchrophasor measurement sensors.