The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey
Abstract
The accurately estimated state is of great importance for maintaining a stable running condition of power systems. To maintain the accuracy of the estimated state, bad data detection (BDD) is utilized by power systems to get rid of erroneous measurements due to meter failures or outside attacks. However, false data injection (FDI) attacks, as recently revealed, can circumvent BDD and insert any bias into the value of the estimated state. Continuous works on constructing and/or protecting power systems from such attacks have been done in recent years. This survey comprehensively overviews three major aspects: constructing FDI attacks; impacts of FDI attacks on electricity market; and defending against FDI attacks. Specifically, we first explore the problem of constructing FDI attacks, and further show their associated impacts on electricity market operations, from the adversary's point of view. Then, from the perspective of the system operator, we present countermeasures against FDI attacks. We also outline the future research directions and potential challenges based on the above overview, in the context of FDI attacks, impacts, and defense.
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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
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
Wang, J., Huang, A., Sung, W., Liu, Y., & Baliga, B. (2009) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2009.932583 · Cited by 260
Cited by
Hossain, E., Khan, I., Un-Noor, F., Sikander, S. S., & Sunny, M. S. H. (2019) · IEEE Access · doi:10.1109/access.2019.2894819 · Cited by 513
Distributed Krein space-based attack detection over sensor networks under deception attacks
Ge, X., Han, Q.-L., Zhong, M., & Zhang, X.-M. (2019) · Automatica · doi:10.1016/j.automatica.2019.108557 · Cited by 341
Machine learning in cybersecurity: a comprehensive survey
Dasgupta, D., Akhtar, Z., & Sen, S. (2022) · The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology · doi:10.1177/1548512920951275 · Cited by 259
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
Liu, X., Qian, C., Hatcher, W. G., Xu, H., Liao, W., & Yu, W. (2019) · IEEE Access · doi:10.1109/access.2019.2920763 · Cited by 152
Metadata
- Authors
- Deng, R., Xiao, G., Lu, R., Liang, H., & Vasilakos, A. V.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2017-04
- Volume
- 13
- Issue
- 2
- Pages
- 411-423
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2525022545
- Cited by
- 553
- References
- 3
- FWCI
- 33.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- Deng, R., Xiao, G., Lu, R., Liang, H., & Vasilakos, A. V. (2017). False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2016.2614396
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