Detection of False Data Injection Attacks in Smart Grids via Nonlinear Interval Observer: A Review

Identify individual False Data Injection (FDI) attacks in different intelligent networks. FDI attacks can circumvent conventional means of identifying corrupt data and interfere with the health assessment process. The rise of FDI attacks has introduced significant security vulnerabilities to smart grids. To address these important issues, A technique for detecting FDI attacks in various SG systems have been proposed. The interval observer is designed to precisely predict the Temporal condition of the internal physical structure based on development of the physical properties of the smart grid system. A detection criterion has been proposed to activate an alert if the remaining interval fails to include zero, considering the inner state boundary and the outer interference boundary. A sensor isolation algorithm based on a logical matrix for assessing attack characteristics is additionally proposed for introducing FDI attacks into sub-areas. Finally, we hope this article complements a series of recent review articles and fosters further research in this rapidly evolving area. And discussed our opinions on remaining challenges and future research directions in this area.