Data Attacks on Power System State Estimation

Abstract

It has shown that with perfect knowledge of the system model and the capability to manipulate a certain number of measurements, the false data injection (FDI) attacks, as a class of data integrity attacks, can coordinate measurements corruption to keep stealth against the bad data detection schemes. However, a more realistic attack is essentially an attack with limited adversarial knowledge of the system model and limited attack resources due to various reasons. In this paper, we generalize the data attacks that they can be pure FDI attacks or combined with availability attacks (e.g., DoS attacks) and analyze the attacks with limited adversarial knowledge or limited attack resources. The attack impact is evaluated by the proposed metrics and the detection probability of attacks is calculated using the distribution property of data with or without attacks. The analysis is supported with results from a power system use case. The results show how important the knowledge is to the attacker and which measurements are more vulnerable to attacks
with limited resources.