Optimal data attacks on power grids: Leveraging detection & measurement jamming

Meter measurements in the power grid are susceptible to manipulation by adversaries that can lead to errors in state estimation. This paper presents a general framework to study attacks on state estimation by adversaries capable of injecting bad-data into measurements and further, of jamming their reception. Through these two techniques, a novel `detectable jamming´ attack is designed that changes the state estimation despite failing bad-data detection checks. Compared to commonly studied `hidden´ data attacks, these attacks have lower costs and a wider feasible operating region. It is shown that the entire domain of jamming costs can be divided into two regions, with distinct graph-cut based formulations for the design of the optimal attack. The most significant insight arising from this result is that the adversarial capability to jam measurements changes the optimal `detectable jamming´ attack design only if the jamming cost is less than half the cost of bad-data injection. A polynomial time approximate algorithm for attack vector construction is developed and its efficacy in attack design is demonstrated through simulations on IEEE test systems.