296 research outputs found
Information-Theoretic Attacks in the Smart Grid
Gaussian random attacks that jointly minimize the amount of information
obtained by the operator from the grid and the probability of attack detection
are presented. The construction of the attack is posed as an optimization
problem with a utility function that captures two effects: firstly, minimizing
the mutual information between the measurements and the state variables;
secondly, minimizing the probability of attack detection via the
Kullback-Leibler divergence between the distribution of the measurements with
an attack and the distribution of the measurements without an attack.
Additionally, a lower bound on the utility function achieved by the attacks
constructed with imperfect knowledge of the second order statistics of the
state variables is obtained. The performance of the attack construction using
the sample covariance matrix of the state variables is numerically evaluated.
The above results are tested in the IEEE 30-Bus test system.Comment: 2017 IEEE International Conference on Smart Grid Communications
(SmartGridComm
Smart meter privacy via the trapdoor channel
A battery charging policy that provides privacy guarantees for smart meter systems with finite capacity battery is proposed. For this policy an upper bound on the information leakage rate is provided. The upper bound applies for general random processes modelling the energy consumption of the user. It is shown that the average energy consumption of the user determines the information leakage rate to the utility provider. The upper bound is shown to be tight by deriving the probability law of a random process achieving the bound
B-Nekrasov matrices and error bounds for linear complementarity problems
The class of B-Nekrasov matrices is a subclass of P-matrices that contains Nekrasov Z-matrices with positive diagonal entries as well as B-matrices. Error bounds for the linear complementarity problem when the involved matrix is a B-Nekrasov matrix are presented. Numerical examples show the sharpness and applicability of the bounds
On the asymptotic optimality of error bounds for some linear complementarity problems
We introduce strong B-matrices and strong B-Nekrasov matrices, for which some error bounds for linear complementarity problems are analyzed. In particular, it is proved that the bounds of García-Esnaola and Peña (Appl. Math. Lett. 22, 1071–1075, 2009) and of (Numer. Algor. 72, 435–445, 2016) are asymptotically optimal for strong B-matrices and strong B-Nekrasov matrices, respectively. Other comparisons with a bound of Li and Li (Appl. Math. Lett. 57, 108–113, 2016) are performed
Data-Injection Attacks
In this chapter we review some of the basic attack constructions that exploit
a stochastic description of the state variables. We pose the state estimation
problem in a Bayesian setting and cast the bad data detection procedure as a
Bayesian hypothesis testing problem. This revised detection framework provides
the benchmark for the attack detection problem that limits the achievable
attack disruption. Indeed, the trade-off between the impact of the attack, in
terms of disruption to the state estimator, and the probability of attack
detection is analytically characterized within this Bayesian attack setting. We
then generalize the attack construction by considering information-theoretic
measures that place fundamental limits to a broad class of detection,
estimation, and learning techniques. Because the attack constructions proposed
in this chapter rely on the attacker having access to the statistical structure
of the random process describing the state variables, we conclude by studying
the impact of imperfect statistics on the attack performance. Specifically, we
study the attack performance as a function of the size of the training data set
that is available to the attacker to estimate the second-order statistics of
the state variables.Comment: arXiv admin note: substantial text overlap with arXiv:1808.0418
When Does Output Feedback Enlarge the Capacity of the Interference Channel?
In this paper, the benefits of channel-output feedback in the Gaussian interference channel (G-IC) are studied under the effect of additive Gaussian noise. Using a linear deterministic (LD) model, the signal to noise ratios (SNRs) in the feedback links beyond which feedback plays a significant role in terms of increasing the individual rates or the sum-rate are approximated. The relevance of this work lies on the fact that it identifies the feedback SNRs for which in any G-IC one of the following statements is true: (a) feedback does not enlarge the capacity region; (b) feedback enlarges the capacity region and the sum-rate is greater than the largest sum-rate without feedback; and (c) feedback enlarges the capacity region but no significant improvement is observed in the sum-rate
Power Injection Measurements are more Vulnerable to Data Integrity Attacks than Power Flow Measurements
A novel metric that describes the vulnerability of the measurements in power
system to data integrity attacks is proposed. The new metric, coined
vulnerability index (VuIx), leverages information theoretic measures to assess
the attack effect on the fundamental limits of the disruption and detection
tradeoff. The result of computing the VuIx of the measurements in the system
yields an ordering of the measurements vulnerability based on the level of
exposure to data integrity attacks. This new framework is used to assess the
measurements vulnerability of IEEE test systems and it is observed that power
injection measurements are overwhelmingly more vulnerable to data integrity
attacks than power flow measurements. A detailed numerical evaluation of the
VuIx values for IEEE test systems is provided.Comment: 6 pages, 9 figures, Submitted to IEEE International Conference on
Communications, Control, and Computing Technologies for Smart Grid
Learning requirements for stealth attacks
The learning data requirements are analyzed for the construction of stealth
attacks in state estimation. In particular, the training data set is used to
compute a sample covariance matrix that results in a random matrix with a
Wishart distribution. The ergodic attack performance is defined as the average
attack performance obtained by taking the expectation with respect to the
distribution of the training data set. The impact of the training data size on
the ergodic attack performance is characterized by proposing an upper bound for
the performance. Simulations on the IEEE 30-Bus test system show that the
proposed bound is tight in practical settings.Comment: International Conference on Acoustics, Speech, and Signal Processing
201
An information theoretic vulnerability metric for data integrity attacks on smart grids
A novel metric that describes the vulnerability of the measurements in power
systems to data integrity attacks is proposed. The new metric, coined
vulnerability index (VuIx), leverages information theoretic measures to assess
the attack effect on the fundamental limits of the disruption and detection
tradeoff. The result of computing the VuIx of the measurements in the system
yields an ordering of their vulnerability based on the level of exposure to
data integrity attacks. This new framework is used to assess the measurement
vulnerability of IEEE 9-bus and 30-bus test systems and it is observed that
power injection measurements are overwhelmingly more vulnerable to data
integrity attacks than power flow measurements. A detailed numerical evaluation
of the VuIx values for IEEE test systems is provided.Comment: 7 pages, 10 figures, submitted to IET Smart Grid. arXiv admin note:
substantial text overlap with arXiv:2207.0697
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