| Title | Statistical Techniques-Based Characterization of FDIA in Smart Grids Considering Grid Contingencies |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Musleh, Ahmed S., Chen, Guo, Dong, Zhao Yang, Wang, Chen, Chen, Shiping |
| Conference Name | 2020 International Conference on Smart Grids and Energy Systems (SGES) |
| Keywords | Canonical correlation analysis (CCA), composability, Computational modeling, Correlation, Cyber-physical security, detection algorithms, false data injection attack (FDIA), Metrics, power grid vulnerability, power grid vulnerability analysis, principal component analysis, Principal Component Analysis (PCA), pubcrawl, Resiliency, Robustness, Sensitivity, Smart grids |
| Abstract | False data injection attack (FDIA) is a real threat to smart grids due to its wide range of vulnerabilities and impacts. Designing a proper detection scheme for FDIA is the 1stcritical step in defending the attack in smart grids. In this paper, we investigate two main statistical techniques-based approaches in this regard. The first is based on the principal component analysis (PCA), and the second is based on the canonical correlation analysis (CCA). The test cases illustrate a better characterization performance of FDIA using CCA compared to the PCA. Further, CCA provides a better differentiation of FDIA from normal grid contingencies. On the other hand, PCA provides a significantly reduced false alarm rate. |
| DOI | 10.1109/SGES51519.2020.00022 |
| Citation Key | musleh_statistical_2020 |
Statistical Techniques-Based Characterization of FDIA in Smart Grids Considering Grid Contingencies