| Title | False Data Injection Attack Location Detection Based on Classification Method in Smart Grid |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Lu, Xiao, Jing, Jiangping, Wu, Yi |
| Conference Name | 2020 2nd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) |
| Date Published | Oct. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-9986-3 |
| Keywords | classification, Classification algorithms, composability, cyber physical systems, Decision Tree, False Data Detection, false data injection attack, Human Behavior, human factors, location detection, Meters, Numerical models, Power measurement, Power systems, pubcrawl, resilience, Resiliency, Smart grid, smart grid security, Smart grids, Topology |
| Abstract | The state estimation technology is utilized to estimate the grid state based on the data of the meter and grid topology structure. The false data injection attack (FDIA) is an information attack method to disturb the security of the power system based on the meter measurement. Current FDIA detection researches pay attention on detecting its presence. The location information of FDIA is also important for power system security. In this paper, locating the FDIA of the meter is regarded as a multi-label classification problem. Each label represents the state of the corresponding meter. The ensemble model, the multi-label decision tree algorithm, is utilized as the classifier to detect the exact location of the FDIA. This method does not need the information of the power topology and statistical knowledge assumption. The numerical experiments based on the IEEE-14 bus system validates the performance of the proposed method. |
| URL | https://ieeexplore.ieee.org/document/9426005 |
| DOI | 10.1109/AIAM50918.2020.00033 |
| Citation Key | lu_false_2020 |
False Data Injection Attack Location Detection Based on Classification Method in Smart Grid