Efficient detection of false data injection attack with invertible automatic encoder and long‐short‐term memory

• Published in: IET cyber-physical systems 2020-03, Vol.5 (1), p.110-118
• Main Authors: Li, Yuancheng, Huo, Wendan, Qiu, Rixuan, Zeng, Jing
• Format: Article
• Language: English
• Subjects: Algorithms; Big Data; Buses; Coders; Deep learning; Electricity distribution; false data injection attack; invertible automatic encoder; long-short-term memory; Machine learning; Methods; power big data training; power engineering computing; power system security; power system state estimation; recurrent neural nets; SCADA systems; Sensors; Smart grid; smart power grids; State estimation; state estimation security; Supervisory control and data acquisition; Test systems; Variables
• ISSN: 2398-3396; 2398-3396
• DOI: 10.1049/iet-cps.2019.0010

The false data injection attack can tamper with the measurement information collected by the Supervisory Control and Data Acquisition, threat the security of state estimation. Therefore, the analysing methods and detection methods of false data injection attacks have important theoretical and practical significance for ensuring the safe operation of smart grids. This study uses the invertible automatic encoder to reduce the dimension of the original data and uses the long‐short‐term memory to detect false data injection attacks. This method overcomes the shortcomings of shallow algorithm and traditional machine learning algorithm for power big data training and avoiding the problems of gradient explosion and gradient disappearing during training. Finally, the authors perform a large number of experiments in the IEEE 118‐node test system and the 300‐node test system and verify the effectiveness of the proposed method.