2,393 research outputs found
An efficient data-driven false data injection attack in smart grids
Data-driven false data injection attack is one of the emerging techniques in smart grids, provided that the adversary can monitor the meter readings. The basic idea is constructing attack vectors from the estimated signal subspace, without knowing system measurement matrix. However, its stealthy performance is significantly influenced by the accuracy of the estimated subspace. Furthermore, it is computationally demanding, because full-size singular value decomposition (SVD) is required for model order selection. In this paper, we propose a truncated SVD based computationally efficient attacking scheme using only the first dominant eigenvector. Both experiment and simulation results are provided to evaluate the performance of the proposed scheme. Compared with the standard false data injection techniques with known measurement matrix, similar stealthy performance is achieved with a reasonable computational complexity
Subspace Methods for Data Attack on State Estimation: A Data Driven Approach
Data attacks on state estimation modify part of system measurements such that
the tempered measurements cause incorrect system state estimates. Attack
techniques proposed in the literature often require detailed knowledge of
system parameters. Such information is difficult to acquire in practice. The
subspace methods presented in this paper, on the other hand, learn the system
operating subspace from measurements and launch attacks accordingly. Conditions
for the existence of an unobservable subspace attack are obtained under the
full and partial measurement models. Using the estimated system subspace, two
attack strategies are presented. The first strategy aims to affect the system
state directly by hiding the attack vector in the system subspace. The second
strategy misleads the bad data detection mechanism so that data not under
attack are removed. Performance of these attacks are evaluated using the IEEE
14-bus network and the IEEE 118-bus network.Comment: 12 page
Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements
The false data injection (FDI) attack cannot be detected by the traditional
anomaly detection techniques used in the energy system state estimators. In
this paper, we demonstrate how FDI attacks can be constructed blindly, i.e.,
without system knowledge, including topological connectivity and line reactance
information. Our analysis reveals that existing FDI attacks become detectable
(consequently unsuccessful) by the state estimator if the data contains grossly
corrupted measurements such as device malfunction and communication errors. The
proposed sparse optimization based stealthy attacks construction strategy
overcomes this limitation by separating the gross errors from the measurement
matrix. Extensive theoretical modeling and experimental evaluation show that
the proposed technique performs more stealthily (has less relative error) and
efficiently (fast enough to maintain time requirement) compared to other
methods on IEEE benchmark test systems.Comment: Keywords: Smart grid, False data injection, Blind attack, Principal
component analysis (PCA), Journal of Computer and System Sciences, Elsevier,
201
False Data Injection Attacks in Smart Grids: State of the Art and Way Forward
In the recent years cyberattacks to smart grids are becoming more frequent
Among the many malicious activities that can be launched against smart grids
False Data Injection FDI attacks have raised significant concerns from both
academia and industry FDI attacks can affect the internal state estimation
processcritical for smart grid monitoring and controlthus being able to bypass
conventional Bad Data Detection BDD methods Hence prompt detection and precise
localization of FDI attacks is becomming of paramount importance to ensure
smart grids security and safety Several papers recently started to study and
analyze this topic from different perspectives and address existing challenges
Datadriven techniques and mathematical modelings are the major ingredients of
the proposed approaches The primary objective of this work is to provide a
systematic review and insights into FDI attacks joint detection and
localization approaches considering that other surveys mainly concentrated on
the detection aspects without detailed coverage of localization aspects For
this purpose we select and inspect more than forty major research contributions
while conducting a detailed analysis of their methodology and objectives in
relation to the FDI attacks detection and localization We provide our key
findings of the identified papers according to different criteria such as
employed FDI attacks localization techniques utilized evaluation scenarios
investigated FDI attack types application scenarios adopted methodologies and
the use of additional data Finally we discuss open issues and future research
direction
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