50,368 research outputs found
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
Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise
In this paper, we consider the problems of state estimation and false data
injection detection in smart grid when the measurements are corrupted by
colored Gaussian noise. By modeling the noise with the autoregressive process,
we estimate the state of the power transmission networks and develop a
generalized likelihood ratio test (GLRT) detector for the detection of false
data injection attacks. We show that the conventional approach with the
assumption of Gaussian noise is a special case of the proposed method, and thus
the new approach has more applicability. {The proposed detector is also tested
on an independent component analysis (ICA) based unobservable false data attack
scheme that utilizes similar assumptions of sample observation.} We evaluate
the performance of the proposed state estimator and attack detector on the IEEE
30-bus power system with comparison to conventional Gaussian noise based
detector. The superior performance of {both observable and unobservable false
data attacks} demonstrates the effectiveness of the proposed approach and
indicates a wide application on the power signal processing.Comment: 8 pages, 4 figures in IEEE Conference on Communications and Network
Security (CNS) 201
Smart Grid Security: Threats, Challenges, and Solutions
The cyber-physical nature of the smart grid has rendered it vulnerable to a
multitude of attacks that can occur at its communication, networking, and
physical entry points. Such cyber-physical attacks can have detrimental effects
on the operation of the grid as exemplified by the recent attack which caused a
blackout of the Ukranian power grid. Thus, to properly secure the smart grid,
it is of utmost importance to: a) understand its underlying vulnerabilities and
associated threats, b) quantify their effects, and c) devise appropriate
security solutions. In this paper, the key threats targeting the smart grid are
first exposed while assessing their effects on the operation and stability of
the grid. Then, the challenges involved in understanding these attacks and
devising defense strategies against them are identified. Potential solution
approaches that can help mitigate these threats are then discussed. Last, a
number of mathematical tools that can help in analyzing and implementing
security solutions are introduced. As such, this paper will provide the first
comprehensive overview on smart grid security
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