@inproceedings{bef051f4557d4f9f8791bd87c0a93ca7,
title = "Detection of false data injection attacks in smart grids using Recurrent Neural Networks",
abstract = "False Data Injection (FDI) attacks create serious security challenges to the operation of power systems, especially when they are carefully constructed to bypass conventional state estimation bad data detection techniques implemented in the power system control room. This paper investigates the utilization of Recurrent Neural Networks (RNN) as a machine learning technique to detect these FDI attacks. The proposed detection algorithm is validated throughout simulations of FDI in power flow data over the span of five years using IEEE-30 Bus system. The simulation results confirm that the proposed RNN-based algorithm achieves high accuracy in detecting anomalies in the data, by observing the temporal variation in the successive data sequence.",
keywords = "Bad data detection, Cyber-security, False Data Injection, Recurrent Neural Networks, Smart grid, State estimation",
author = "Abdelrahman Ayad and Farag, {Hany E.Z.} and Amr Youssef and El-Saadany, {Ehab F.}",
note = "Funding Information: This publication was made possible by NPRP grant no. NPRP 9-055-2-022 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2018 ; Conference date: 19-02-2018 Through 22-02-2018",
year = "2018",
month = jul,
day = "3",
doi = "10.1109/ISGT.2018.8403355",
language = "British English",
series = "2018 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "2018 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2018",
address = "United States",
}
