False Data Injection Attack on Atmospheric Electric Field in Thunderstorm Warning

Publication Date

1-1-2022

Document Type

Conference Proceeding

Publication Title

Proceedings - 2022 International Conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2022

DOI

10.1109/CCPQT56151.2022.00045

First Page

219

Last Page

223

Abstract

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.

Funding Number

2022YFH0098

Funding Sponsor

Sichuan Province Science and Technology Support Program

Keywords

atmospheric electric field (AEF), False data injection attack (FDIA), principal component analysis (PCA), thunderstorm warning

Department

Applied Data Science

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