Abstarct: In power systems, state variables include voltage values and phase angles in the system nodes. Measurements are required to estimate system performance in real-time operation for both reliability control and economic load dispatch. The state estimation is the most important part of the monitoring of the power grid, in which the state of the system is determined and the operator is able to make the appropriate decision about the possible actions necessary to maintain the system's performance in a normal and reliable manner. To perform a state estimation, measurement data from Remote Terminal Units is sent to the power control system. Meanwhile, if the attacker attacks the information sent to the control center in some way, it can cause an estimation error in the estimator. In this thesis, we propose a method for identifying bad data injections to the smart grid in a power system. In the first step, the proposed method uses the historical data of the power system and it calculates the deviation of the system's measurements and their histogram function. In the next step, using the proposed detection function through the histogram function calculated in the previous step, bad data injection attacks are detected. The proposed method performs a separate detection for each measurement. Therefore, according to the network structure, it is able to identify the target of an attacker.