Data Interpolation for Mitigating False Data Injection Attacks in Smart Grids

Smart grid generation systems are integral to modern energy infrastructure, enabling efficient energy management and distribution. However, these systems are vulnerable to cyber threats. In this paper, we have investigated False Data Injection Attacks (FDIA), which can compromise the quality and reliability of generated data. This research paper presents an approach to address the data missing or incomplete problem in smart grid power generation systems due to FDIA attacks. We develop a predictive model to accurately forecast the actual values of corrupted data points and restore to match with the original database. The MSE of 440676.6157 before data reconstruction and reduced MSE 358329.9269 after reconstruction in the result demonstrate high accuracy and efficiency, highlighting the potential of this approach to enhance the security and resilience of smart grid systems against cyber threats.