A Survey of Machine Learning Methods for Detecting False Data Injection
Attacks in Power Systems

A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems

16 August 2020

Ioannis Zografopoulos

XiaoRui Liu

Charalambos Konstantinou

Papers citing "A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems"

6 / 6 papers shown

Title
Expert enhanced dynamic time warping based anomaly detection Matej Kloska Gabriela Grmanová Viera Rozinajová 29 20 0 02 Oct 2023
False Data Injection Attacks in Smart Grids: State of the Art and Way Forward Muhammad Irfan Alireza Sadighian Adeen Tanveer Shaikha J. Al‐Naimi Gabriele Oligeri 29 8 0 20 Aug 2023
An Approach of Replicating Multi-Staged Cyber-Attacks and Countermeasures in a Smart Grid Co-Simulation Environment Ömer Sen D. Velde Sebastian N. Peters Martin Henze 14 9 0 05 Oct 2021
Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks Osman Boyaci M. Narimani K. Davis Muhammad Ismail T. Overbye E. Serpedin 43 90 0 24 Apr 2021
CHIMERA: A Hybrid Estimation Approach to Limit the Effects of False Data Injection Attacks Xiaorui Liu Yaodan Hu Charalambos Konstantinou Yier Jin AAML 17 3 0 25 Mar 2021
Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey Praveen Kumar Viet Quoc Pham Madhusanka Liyanage N. Deepa Mounik Vvss Shivani Reddy Reddy Maddikunta Neelu Khare Thippa Reddy Gadekallu W. Hwang 41 56 0 20 Jan 2021