Survey of machine learning methods for detecting false data injection attacks in power systems

Survey of machine learning methods for detecting false data injection attacks in power systems

Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber-attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs t...

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Bibliographic Details
Main Authors: Ali Sayghe, Yaodan Hu, Ioannis Zografopoulos, XiaoRui Liu, Raj Gautam Dutta, Yier Jin, Charalambos Konstantinou
Format: Article
Language:English
Published: Wiley 2020-10-01
Series:IET Smart Grid
Subjects:
security of data
power grids
power system security
power engineering computing
power system measurement
energy management systems
power system state estimation
binary decision diagrams
learning (artificial intelligence)
system data
energy management system
unknown state variables
system redundant measurements
data detection algorithms
fdia
malicious data vectors
data-driven solutions
machine learning algorithms
sensor data
power system se algorithms
false data injection attacks
power systems
cyber attacks
cyber-attacks
power grid monitoring systems
Online Access:https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2020.0015

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https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2020.0015

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