  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 with
perfect knowledge of the system model and the capability to manipulate a
certain number of measurements, the FDI attacks can coordinate measurements
corruption to keep stealth against the bad data detection. 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.
