A novel method to detect bad data injection attack in smart grid

By Ting Liu, Yun Gu, Dai Wang, Yuhong Gui, X. Guan. 2013

53 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Rongxing Lu
2. Xuemin Shen
3. Athanasios V. Vasilakos
4. Rong Jiang
5. Changxiang Shen
6. Yeyu Wang
7. Jun Luo
8. Xiaodong Lin

In Computer Networks and Communications

1. Rongxing Lu
2. Xuemin Shen
3. Gaoxi Xiao
4. Athanasios V. Vasilakos
5. Jun Luo
6. Ruilong Deng
7. Xiaodong Lin

In Control and Systems Engineering

1. Gaoxi Xiao
2. Rongxing Lu
3. Xuemin Shen
4. Ruilong Deng

Citations in the corpus

• Energy-theft detection issues for advanced metering infrastructure in smart grid (2014, methodology): “In order to detect more bad data injection and locate the bad data within a smaller area, an Adaptive Partitioning State Estimation (APSE) is proposed[45].”
• False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey (2017, background): “More references on defending against FDI attacks can be found in [58]–[66].”