Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid
By Mohammad Esmalifalak, Lanchao Liu, N. Nguyen, Rong Zheng, Zhu Han. IEEE Systems Journal, 2017
558 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/c08db078d5dd093134383352f650968846de5b17
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
- Ekram Hossain
- Syed Ali Hassan
- James Jian Qiao Yu
- Victor O. K. Li
- Rasheed Hussain
- Yunhe Hou
- Fatima Hussain
- Kyunghyun Cho
In Computer Networks and Communications
- Syed Ali Hassan
- Ekram Hossain
- James Jian Qiao Yu
- Victor O. K. Li
- Rasheed Hussain
- Yunhe Hou
- Fatima Hussain
- Hesham Elsawy
In Control and Systems Engineering
Citations in the corpus
- A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids (2020, unknown): “SVM’s simplicity is the main reason for its frequent utilization in detecting FDIA as illustrated in [92], [93], [94], [95], [96], [97], [98], [99], [100].”
- Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, methodology): “[182], [183] use SVM and PCA to detect false data injection and stealthy attacks in smart grid.”
- Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks (2018, background): “In addition, the proposed mechanism extracts not only spatial (as did in [24] and [26]) but also temporal power system dynamic features for attack detection, which is novel and effective.”