False Data Injection Attack on Atmospheric Electric Field in Thunderstorm Warning
Proceedings - 2022 International Conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2022
Thunderstorm warning plays an important role in lightning prevention and disaster mitigation. In practical applications, thunderstorm warning system is also vulnerable to attacks, such as False Data Injection Attack (FDIA). However, there is a lack of research on False Data Injection Attack for thunderstorm warning. Therefore, this paper put forwards a FDIA method based on principal component analysis (PCA) for atmospheric electric field (AEF), which is usually used for thunderstorm warning. In the FDIA scenario, the AEF-based thunderstorm warning algorithm is also introduced with electric field differential index (EFDI). Finally, experiments are conducted based on AEF data collected by an atmospheric electric field meter (AEFM) about the real thunderstorm. The experimental results show that FDIA seriously interferes with the results of the AEF-based thunderstorm warning.
Sichuan Province Science and Technology Support Program
atmospheric electric field (AEF), False data injection attack (FDIA), principal component analysis (PCA), thunderstorm warning
Applied Data Science
Xiang Li, Kadhim Hayawi, Yi Chen, Shih Yu Chang, Hong Wen, Pin Han Ho, Ling Yang, and Qiyuan Yin. "False Data Injection Attack on Atmospheric Electric Field in Thunderstorm Warning" Proceedings - 2022 International Conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2022 (2022): 219-223. https://doi.org/10.1109/CCPQT56151.2022.00045