TY - GEN
T1 - Limiting false data attacks on power system state estimation
AU - Kosut, Oliver
AU - Jia, Liyan
AU - Thomas, Robert J.
AU - Tong, Lang
PY - 2010
Y1 - 2010
N2 - Malicious attacks against power system state estimation are considered. It has been recently observed that if an adversary is able to manipulate the measurements taken at several meters in a power system, it can sometimes change the state estimate at the control center in a way that will never be detected by classical bad data detectors. However, in cases when the adversary is not able to perform this attack, it was not clear what attacks might look like. An easily computable heuristic is developed to find bad adversarial attacks in all cases. This heuristic recovers the undetectable attacks, but it will also find the most damaging attack in all cases. In addition, a Bayesian formulation of the bad data problem is introduced, which captures the prior information that a control center has about the likely state of the power system. This formulation softens the impact of undetectable attacks. Finally, a new L∞ norm detector is introduced, and it is demonstrated that it outperforms more standard L2 norm based detectors by taking advantage of the inherent sparsity of the false data injection.
AB - Malicious attacks against power system state estimation are considered. It has been recently observed that if an adversary is able to manipulate the measurements taken at several meters in a power system, it can sometimes change the state estimate at the control center in a way that will never be detected by classical bad data detectors. However, in cases when the adversary is not able to perform this attack, it was not clear what attacks might look like. An easily computable heuristic is developed to find bad adversarial attacks in all cases. This heuristic recovers the undetectable attacks, but it will also find the most damaging attack in all cases. In addition, a Bayesian formulation of the bad data problem is introduced, which captures the prior information that a control center has about the likely state of the power system. This formulation softens the impact of undetectable attacks. Finally, a new L∞ norm detector is introduced, and it is demonstrated that it outperforms more standard L2 norm based detectors by taking advantage of the inherent sparsity of the false data injection.
KW - False data attack
KW - Power system security
KW - Power system state estimation
UR - http://www.scopus.com/inward/record.url?scp=77953719713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953719713&partnerID=8YFLogxK
U2 - 10.1109/CISS.2010.5464816
DO - 10.1109/CISS.2010.5464816
M3 - Conference contribution
AN - SCOPUS:77953719713
SN - 9781424474172
T3 - 2010 44th Annual Conference on Information Sciences and Systems, CISS 2010
BT - 2010 44th Annual Conference on Information Sciences and Systems, CISS 2010
T2 - 44th Annual Conference on Information Sciences and Systems, CISS 2010
Y2 - 17 March 2010 through 19 March 2010
ER -