TY - GEN

T1 - Distributed models for sparse attack construction and state vector estimation in the smart grid

AU - Ozay, Mete

AU - Esnaola, Inaki

AU - Yarman Vural, Fatos T.

AU - Kulkarni, Sanjeev R.

AU - Vincent Poor, H.

PY - 2012

Y1 - 2012

N2 - Two distributed attack models and two distributed state vector estimation methods are introduced to handle the sparsity of smart grid networks in order to employ unobservable false data injection attacks and estimate state vectors. First, Distributed Sparse Attacks in which attackers process local measurements in order to achieve consensus for an attack vector are introduced. In the second attack model, called Collective Sparse Attacks, it is assumed that the topological information of the network and the measurements is available to attackers. However, attackers employ attacks to the groups of state vectors. The first distributed state vector estimation method, called Distributed State Vector Estimation, assumes that observed measurements are distributed in groups or clusters in the network. The second method, called Collaborative Sparse State Vector Estimation, consists of different operators estimating subsets of state variables. Therefore, state variables are assumed to be distributed in groups and accessed by the network operators locally. The network operators compute their local estimates and send the estimated values to a centralized network operator in order to update the estimated values.

AB - Two distributed attack models and two distributed state vector estimation methods are introduced to handle the sparsity of smart grid networks in order to employ unobservable false data injection attacks and estimate state vectors. First, Distributed Sparse Attacks in which attackers process local measurements in order to achieve consensus for an attack vector are introduced. In the second attack model, called Collective Sparse Attacks, it is assumed that the topological information of the network and the measurements is available to attackers. However, attackers employ attacks to the groups of state vectors. The first distributed state vector estimation method, called Distributed State Vector Estimation, assumes that observed measurements are distributed in groups or clusters in the network. The second method, called Collaborative Sparse State Vector Estimation, consists of different operators estimating subsets of state variables. Therefore, state variables are assumed to be distributed in groups and accessed by the network operators locally. The network operators compute their local estimates and send the estimated values to a centralized network operator in order to update the estimated values.

KW - Smart grid security

KW - attack detection

KW - distributed optimization

KW - false data injection

KW - sparse models

UR - http://www.scopus.com/inward/record.url?scp=84876031093&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876031093&partnerID=8YFLogxK

U2 - 10.1109/SmartGridComm.2012.6486001

DO - 10.1109/SmartGridComm.2012.6486001

M3 - Conference contribution

AN - SCOPUS:84876031093

SN - 9781467309110

T3 - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012

SP - 306

EP - 311

BT - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012

T2 - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012

Y2 - 5 November 2012 through 8 November 2012

ER -