The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Review of False Data Injection Attacks Against Modern Power Systems
Abstract
With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks
Hug, G., & Giampapa, J. A. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2195338 · Cited by 595
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The 2015 Ukraine Blackout: Implications for False Data Injection Attacks
Liang, G., Weller, S. R., Zhao, J., Luo, F., & Dong, Z. Y. (2017) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2016.2631891 · Cited by 1,248
Self-citationDetecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach
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State of the art of cyber-physical systems security: An automatic control perspective
Zacchia Lun, Y., D’Innocenzo, A., Smarra, F., Malavolta, I., & Di Benedetto, M. D. (2019) · Journal of Systems and Software · doi:10.1016/j.jss.2018.12.006 · Cited by 211
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Metadata
- Authors
- Liang, G., Zhao, J., Luo, F., Weller, S. R., & Dong, Z. Y.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2017-07
- Volume
- 8
- Issue
- 4
- Pages
- 1630-1638
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2319367234
- Cited by
- 1,028
- References
- 2
- FWCI
- 56.12
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liang, G., Zhao, J., Luo, F., Weller, S. R., & Dong, Z. Y. (2017). A Review of False Data Injection Attacks Against Modern Power Systems. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2015.2495133
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