12 research outputs found
Stealth attacks on the smart grid
Random attacks that jointly minimize the amount of information acquired by the operator about the state of the grid and the probability of attack detection are presented. The attacks minimize the information acquired by the operator by minimizing the mutual information between the observations and the state variables describing the grid. Simultaneously, the attacker aims to minimize the probability of attack detection by minimizing the Kullback-Leibler (KL) divergence between the distribution when the attack is present and the distribution under normal operation. The resulting cost function is the weighted sum of the mutual information and the KL divergence mentioned above. The trade-off between the probability of attack detection and the reduction of mutual information is governed by the weighting parameter on the KL divergence term in the cost function. The probability of attack detection is evaluated as a function of the weighting parameter. A sufficient condition on the weighting parameter is given for achieving an arbitrarily small probability of attack detection. The attack performance is numerically assessed on the IEEE 14-Bus, 30-Bus, and 118-Bus test systems
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
Information Theoretic Data Injection Attacks with Sparsity Constraints
Information theoretic sparse attacks that minimize simultaneously the
information obtained by the operator and the probability of detection are
studied in a Bayesian state estimation setting. The attack construction is
formulated as an optimization problem that aims to minimize the mutual
information between the state variables and the observations while guaranteeing
the stealth of the attack. Stealth is described in terms of the
Kullback-Leibler (KL) divergence between the distributions of the observations
under attack and without attack. To overcome the difficulty posed by the
combinatorial nature of a sparse attack construction, the attack case in which
only one sensor is compromised is analytically solved first. The insight
generated in this case is then used to propose a greedy algorithm that
constructs random sparse attacks. The performance of the proposed attack is
evaluated in the IEEE 30 Bus Test Case.Comment: Submitted to SGC 202
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
Information Theoretic Data Injection Attacks with Sparsity Constraints
International audienceInformation theoretic sparse attacks that minimize simultaneously the information obtained by the operator and the probability of detection are studied in a Bayesian state estimation setting. The attack construction is formulated as an optimization problem that aims to minimize the mutual information between the state variables and the observations while guaranteeing the stealth of the attack. Stealth is described in terms of the Kullback-Leibler (KL) divergence between the distributions of the observations under attack and without attack. To overcome the difficulty posed by the combinatorial nature of a sparse attack construction, the attack case in which only one sensor is compromised is analytically solved first. The insight generated in this case is then used to propose a greedy algorithm that constructs random sparse attacks. The performance of the proposed attack is evaluated in the IEEE 30 Bus Test Case
An information theoretic metric for measurement vulnerability to 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 in terms of 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 degree 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 significantly more vulnerable to data integrity attacks than power flow measurements. A detailed numerical evaluation of the VuIx values for IEEE test systems is provided
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
Information-Theoretic Data Injection Attacks on the Smart Grid
In this thesis, we use information-theoretic measures to quantify the caused disruption and the probability of detection of the data injection attacks. Specifically the attacker minimizes the mutual information between the state variables and the compromised measurements to minimize the amount of information acquired by the operator from the measurements about the state variables. Also the attacker minimizes the Kullback-Leibler divergence between the distribution of measurements with attack and without attack to minimize the probability of detection.
The stealth attacks achieve these two contradictive objectives by minimizing the equal sum of them, which is generalized to the weighted sum later. Closed-form expression for the optimal Gaussian attack is proposed for the stealth attacks and the generalized stealth attacks when the attacker prioritizes the probability of detection over the disruption. Additionally, a closed-form expression of the probability of detection is obtained. To inform the design guidelines for the corresponding weighting parameter, a concentration inequality upper bound is proposed for the probability of detection.
RMT tools are used to characterize the ergodic performance of the attacks when the attacker only gets access to a limited number of samples of the state variables. For the non-asymptotic scenario, an upper bound is proposed for the ergodic performance, for which a simple convex optimization needs to be solved to compute it. For the asymptotic case, a closed-form expression is provided for the ergodic performance of the attacks