Malicious Data Attacks on Smart Grid State Estimation: Attack Strategies and Countermeasures

By Oliver Kosut, Liyan Jia, Robert J. Thomas, Lang Tong. 2010 First IEEE International Conference on Smart Grid Communications, 2010

387 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/62ccbcca8c365259c4ffb511e39a7901b9d53ec0

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

In Computer Networks and Communications

In Control and Systems Engineering

Citations in the corpus

Attacks against process control systems: risk assessment, detection, and response (2011, background): “Notable exceptions are the study of false data-injection attacks to state estimation in power grids [40–45], and electricity markets [46].”
Cyber security in the Smart Grid: Survey and challenges (2013, background): “Since state estimation can be performed only when all data is sufficiently collected from local-area systems [39] [43], such small-scale DoS attacks can result in partial unavailability of data samples for state estimation.”
Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks (2012, background): “SCADA RTUs forward sensor measurements from points of the transmission grid to a control center so that the state of the system, given by voltage magnitudes and angles at the buses of the system, can be estimated.”
Integrity Data Attacks in Power Market Operations (2011, background): “In [10] and [11] computationally efficient strategies have been developed to detect these malicious data attacks against state estimators.”
A secure control framework for resource-limited adversaries (2012, neutral): “The work in [17]”
False data injection attacks against state estimation in electric power grids (2011, background): “The preliminary version of this article [Liu et al. 2009] has attracted several research groups to investigate how to defend against false data injection attacks (e.g., [Bobba et al. 2010; Sandberg et al. 2010; Kosut et al. 2010a; 2010c; 2010b; D´ an and Sandberg 2010]).”
Cyber Security and Privacy Issues in Smart Grids (2012, background): “The authors in [58] adopted a graph theoretic approach to detect and localize attacks of state estimations.”
A Survey of Physics-Based Attack Detection in Cyber-Physical Systems (2018, background): “[50] consider attackers trying to minimize the error introduced in the estimate, and defenders with a new detection algorithm that attempts to detect false data injection attacks.”
Attack models and scenarios for networked control systems (2012, background): “The work in [12] considered stealthy attacks with limited resources and proposed improved detection methods, while [16] analyzed the minimum number of sensors required for stealthy attacks, based on which measurement security metrics were proposed.”
False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey (2017, background): “The problem is addressed by the adversary from a decision theoretic point of view [33]–[35].”
Security analysis for Cyber-Physical Systems against stealthy deception attacks (2013, background): “Some papers also proposed the security indices or vulnerability conditions which allow the attacker to perform an elaborate deception attack [12], [13].”