Name
Abstract | ||
|---|---|---|
A new formulation for detection of false data injection attacks in the smart grid is introduced. The attack detection problem is posed as a statistical learning problem in which the observed measurements are classified as being either attacked or secure. The proposed approach provides an attack detection framework that surmounts over the constraints arising due to the sparse structure of the problem and implicitly exploits any available prior knowledge about the system. Specifically, three supervised learning algorithms are presented. These procedures operate by first observing the power system in order to construct a training dataset which is later used to detect the attacks in new observations. In order to assess the validity of the proposed techniques, the behavior of the proposed algorithms is examined on IEEE test systems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SmartGridComm.2012.6486002 | Smart Grid Communications |
Keywords | Field | DocType |
IEEE standards,learning (artificial intelligence),power system measurement,power system security,smart power grids,statistical analysis,IEEE test system,false data injection attack detection,power system measurement,smart grid,smarter security,statistical learning problem,supervised learning algorithm,training dataset construction,Smart grid security,attack detection,classification,convex optimization,machine learning | Data mining,Injection attacks,Smart grid,Computer science,Electric power system,Exploit,Power system security,Statistical learning,Supervised training,Statistical analysis | Conference |
ISSN | ISBN | Citations |
2373-6836 | 978-1-4673-0909-7 | 5 |
PageRank | References | Authors |
0.57 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mete Ozay | 1 | 106 | 14.50 |
| Inaki Esnaola | 2 | 87 | 10.43 |
| Fatos T. Yarman-Vural | 3 | 287 | 27.11 |
| S. R. Kulkarni | 4 | 2105 | 360.73 |
| H. V. Poor | 5 | 25411 | 1951.66 |
| Yarman Vural, F.T. | 6 | 73 | 6.17 |