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A class of data integrity attack, known as false data injection (FDI) attack,has been studied with a considerable amount of work. It has shown that withperfect knowledge of the system model and the capability to manipulate acertain number of measurements, the FDI attacks can coordinate measurementscorruption to keep stealth against the bad data detection. However, a morerealistic attack is essentially an attack with limited adversarial knowledge ofthe system model and limited attack resources due to various reasons. In thispaper, we generalize the data attacks that they can be pure FDI attacks orcombined with availability attacks (e.g., DoS attacks) and analyze the attackswith limited adversarial knowledge or limited attack resources. The attackimpact is evaluated by the proposed metrics and the detection probability ofattacks is calculated using the distribution property of data with or withoutattacks. The analysis is supported with results from a power system use case.The results show how important the knowledge is to the attacker and whichmeasurements are more vulnerable to attacks with limited resources.

Data Attacks on Power System State Estimation: Limited Adversarial Knowledge vs. Limited Attack Resources