Name
Title | ||
|---|---|---|
A Statistical Framework for Detecting Electricity Theft Activities in Smart Grid Distribution Networks |
Abstract | ||
|---|---|---|
Electricity distribution networks have undergone rapid change with the introduction of smart meter technology, that have advanced sensing and communications capabilities, resulting in improved measurement and control functions. However, the same capabilities have enabled various cyber-attacks. A particular attack focuses on electricity theft, where the attacker alters (increases) the electricity consumption measurements recorded by the smart meter of other users, while reducing her own measurement. Thus, such attacks, since they maintain the total amount of power consumed at the distribution transformer are hard to detect by techniques that monitor mean levels of consumption patterns. To address this data integrity problem, we develop statistical techniques that utilize information on higher order statistics of electricity consumption and thus are capable of detecting such attacks and also identify the users (attacker and victims) involved. The models work both for independent and correlated electricity consumption streams. The results are illustrated on synthetic data, as well as emulated attacks leveraging real consumption data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JSAC.2019.2952181 | IEEE Journal on Selected Areas in Communications |
Keywords | Field | DocType |
Smart meters,Loss measurement,Power measurement,Area measurement,Meters,Time measurement | Smart grid,Electricity,Computer science,Distribution networks,Computer network | Journal |
Volume | Issue | ISSN |
38 | 1 | 0733-8716 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Tao | 1 | 0 | 0.34 |
| George Michailidis | 2 | 303 | 35.19 |