15 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
Machine Learning Methods for Attack Detection in the Smart Grid
Attack detection problems in the smart grid are posed as statistical learning
problems for different attack scenarios in which the measurements are observed
in batch or online settings. In this approach, machine learning algorithms are
used to classify measurements as being either secure or attacked. An attack
detection framework is provided to exploit any available prior knowledge about
the system and surmount constraints arising from the sparse structure of the
problem in the proposed approach. Well-known batch and online learning
algorithms (supervised and semi-supervised) are employed with decision and
feature level fusion to model the attack detection problem. The relationships
between statistical and geometric properties of attack vectors employed in the
attack scenarios and learning algorithms are analyzed to detect unobservable
attacks using statistical learning methods. The proposed algorithms are
examined on various IEEE test systems. Experimental analyses show that machine
learning algorithms can detect attacks with performances higher than the attack
detection algorithms which employ state vector estimation methods in the
proposed attack detection framework.Comment: 14 pages, 11 Figure