Designing false data injection attacks penetrating AC‐based bad data detection system and FDI dataset generation. (17th August 2020)

• Record Type: Journal Article
• Authors: Tran, Nam N.; Pota, Hemanshu R.; Tran, Quang N.; Yin, Xuefei; Hu, Jiankun
• Is Part Of: Concurrency and computation. Volume 34:Number 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-17
• Subjects: bad data detector -- cyber‐physical system -- cyberthreat -- false data injection attack -- power system -- smart grid -- state estimation; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5956
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The evolution of the traditional power system toward the modern smart grid has posed many new cybersecurity challenges to this critical infrastructure. One of the most dangerous cybersecurity threats is the false data injection (FDI) attack, especially when it is capable of completely bypassing the widely deployed bad data detector of state estimation (SE) and interrupting the normal operation of the power system. Most of the simulated FDI attacks are designed using a simplified linearized DC model, while most of the industry‐standard SE systems are based on the nonlinear AC model. In this article, a comprehensive FDI attack scheme is presented based on the nonlinear AC model. A case study of the nine‐bus Western System Coordinated Council (WSCC)'s power system is provided, using an industry‐standard package to assess the outcomes of the proposed design scheme. A public FDI dataset is generated as a test set for the community to develop and evaluate new detection algorithms, which are lacking in the field. The FDI's stealthy quality of the dataset is assessed and proven through a preliminary analysis based on both physical power law and statistical analysis.