The normal operation of power system relies on accurate state estimation that faithfully reflects the physical aspects of the electrical power grids. However, recent research shows that carefully synthesized false-data injection attacks can bypass the security system and thus introduce arbitrary errors to state estimates. In this paper, we investigate defending mechanisms against false-data injection attacks . By protecting carefully selected meter measurements and (or) power network topological information, we show that no false-data injection attack can be formulated to compromise any set of state estimates. We characterize the optimal protection problem  as a variant Steiner tree problem in a graph , and propose both exact and reduced-complexity approximation algorithms to derive the optimal protection strategy that achieves the protection objective with minimum cost. For practical implementation, we also develop a unified defending strategy that efficiently utilizes both secure meter measurements and covert topological information .  The advantageous performance of the proposed defending mechanisms is verified in IEEE standard power system testcases . In both theory and practice, our results provide solid countermeasures against false-data injection attack in large-scale electrical power system, which will be useful in the security upgrade projects towards smart power grids .
The normal operation of power system relies on accurate state estimation that faithfully reflects the physical aspects of the electrical power grids. However, recent research shows that carefully synthesized false-data injection attacks can bypass the security system and introduce arbitrary errors to state estimates. In this paper, we use graphical methods to study defending mechanisms against false-data injection attacks on power system state estimation. By securing carefully selected meter measurements , no false data injection attack can be launched to compromise any set of state estimates. We characterize the optimal protection problem , which protects the state estimates with minimum number of measurements, as a variant Steiner tree problem in a graph . Based on the graphical characterization, we propose both exact and reduced-complexity approximation algorithms . In particular, we show that the proposed tree-pruning based approximation algorithm significantly reduces computational complexity, while yielding negligible performance degradation compared with the optimal algorithms. The advantageous performance of the proposed defending mechanisms is verified in IEEE standard power system testcases .
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to derive the optimal protection strategy that achieves the protection objective with minimum cost. For practical implementation, we also develop a unified defending strategy that efficiently utilizes both secure meter measurements and covert topological information
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In particular, we show that the proposed tree-pruning based approximation algorithm significantly reduces computational complexity, while yielding negligible performance degradation compared with the optimal algorithms.
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. In both theory and practice, our results provide solid countermeasures against false-data injection attack in large-scale electrical power system, which will be useful in the security upgrade projects towards smart power grids