False Data Injection Attack on Atmospheric Electric Field in Thunderstorm Warning
Source of Publication
2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT)
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.
Meters, Quantum computing, Atmospheric modeling, Lightning, Alarm systems, Data models, Computer crashes
Li, Xiang; Hayawi, Kadhim; Chen, Yi; Chang, Shih Yu; Wen, Hong; Ho, Pin-Han; Yang, Ling; and Yin, Qiyuan, "False Data Injection Attack on Atmospheric Electric Field in Thunderstorm Warning" (2022). All Works. 5472.
Indexed in Scopus