The majority of independent robotic generation control systems, intelligent electronic equipment, measuring devices, communication networks, and remote terminal units (RTU) are all susceptible to cyber-attacks and fraudulent data injections. Blackouts are caused by the aggressive behavior of others of defensive devices brought on by this bogus data injection. One of the security issues with sophisticated grid is false data injection. This research suggests using a game-theoretic method in conjunction with risk management strategies. The risk assessment technique is used to determine the significance of erroneous data intrusion. The previously discussed method's conditional value at risk (CVaR) metric calculates the load-sharing defender's loss caused by erroneous data inoculations. The stochastic security game model is then fed measured risks as an exciting parameter. Solving the security game yields decisions depending on defensive measures. As a result, the security game model provides an in-depth collection of instructions for choosing the optimal mitigation techniques to lower the chance of attacker false data injections. False information data detection methods can be used to find bogus data. The cluster-based strategy and the threshold-based algorithm are contrasted. The project places importance on reducing the stabilizing time (f) for frequency changes. The frequency change (f) stabilizing time takes longer using the sample RGC controller. Ant colony optimization (ACO) methods are implemented to solve this issue, increasing stability time. The proposed method was created using MATLAB/SIMULINK, and it has been observed that it can produce better results.