| Title | Detection of False Data Injection Attack in Smart Grid Using Decomposed Nearest Neighbor Techniques |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Pedramnia, Kiyana, Shojaei, Shayan |
| Conference Name | 2020 10th Smart Grid Conference (SGC) |
| Date Published | Dec. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-6654-1957-4 |
| Keywords | Classification algorithms, Convex functions, Decomposed Nearest Neighbor algorithm, FDI attacks, machine learning, machine learning algorithms, Measurement, Metrics, metrics testing, phasor measurement units, pubcrawl, Smart grid, Smart grids, Voltage measurement |
| Abstract | Smart grid communication system deeply rely on information technologies which makes it vulnerable to variable cyber-attacks. Among possible attacks, False Data Injection (FDI) Attack has created a severe threat to smart grid control system. Attackers can manipulate smart grid measurements such as collected data of phasor measurement units (PMU) by implementing FDI attacks. Detection of FDI attacks with a simple and effective approach, makes the system more reliable and prevents network outages. In this paper we propose a Decomposed Nearest Neighbor algorithm to detect FDI attacks. This algorithm improves traditional k-Nearest Neighbor by using metric learning. Also it learns the local-optima free distance metric by solving a convex optimization problem which makes it more accurate in decision making. We test the proposed method on PMU dataset and compare the results with other beneficial machine learning algorithms for FDI attack detection. Results demonstrate the effectiveness of the proposed approach. |
| URL | https://ieeexplore.ieee.org/document/9335732 |
| DOI | 10.1109/SGC52076.2020.9335732 |
| Citation Key | pedramnia_detection_2020 |
Detection of False Data Injection Attack in Smart Grid Using Decomposed Nearest Neighbor Techniques