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Study of Monitoring False Data Injection Attacks Based on Machine-learning in Electric Systems

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DOI: 10.23977/jeis.2017.22013 | Downloads: 91 | Views: 5598


Baoyi Wang 1, Yadong Zhao 1, Shaomin Zhang 1, Bihe Li 1


1 School of control and Computer Engineering, North China Electric Power University, Baoding 071003, Hebei Province, China

Corresponding Author

Baoyi Wang


False data injected by hackers can interfere with power system state estimation and pose a great threat to the safe and reliable operation of modern power systems (FDIA). The traditional bad data detection method can not effectively detect such attacks. In this paper, by extracting relevant power system measurement characteristic value and use the historical data as the sample, using three classical machine learning algorithms (Perceptron, KNN, SVM) of false data injection attack detection, and respectively in IEEE-9, IEEE-57, IEEE-118 simulation platform for test, verify the supervised machine learning algorithm is applied to the validity of false data injection attack detection.


FDIA, Machine Learning, Supervised Learning


Baoyi, W., Yadong, Z., Shaomin, Z., Bihe, L. (2017) Study of Monitoring False Data Injection Attacks Based on Machine-learning in Electric Systems. Journal of Electronics and Information Science (2017) Vol.2, Num.1: 122-128.


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