Defending Against False Data Injection Attacks on Power System State Estimation

By Ruilong Deng, Gaoxi Xiao, Rongxing Lu. IEEE Transactions on Industrial Informatics, 2017

294 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9b25a2453eab5c669de2075417728d455f2ad013

How researchers see this paper

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

In Control and Systems Engineering

1. Qing-Long Han
2. Xiaohua Ge
3. Derui Ding
4. Zidong Wang
5. Yunhe Hou
6. James Jian Qiao Yu
7. Victor O. K. Li
8. Xianming Zhang

In Electrical and Electronic Engineering

1. Xiaohua Ge
2. Yunhe Hou
3. Qing-Long Han
4. James Jian Qiao Yu
5. Victor O. K. Li
6. Gaoxi Xiao
7. Rongxing Lu
8. Athanasios V. Vasilakos

Citations in the corpus

• Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks (2018, background): “attacks such as distributed denial of service and jamming, successful FDIA can circumvent the conventional residual-based bad data detection mechanism [10].”
• False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey (2017, background): “However, potential threats in terms of cyber attacks would be introduced into the system [9]–[18].”
• A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems (2019, background): “Recently, from the perspective of defenders, the effect on state estimation from attacks was investigated, see [143], [144] and the references therein.”