Data Injection Attack

False data injection attacks (FDIAs) target smart grids and other cyber-physical systems by injecting malicious data into measurement systems, compromising data integrity and potentially causing system instability or failures. Current research focuses on developing robust detection and localization methods, employing machine learning techniques such as graph neural networks (GNNs), Long Short-Term Memory (LSTM) networks, and causal inference algorithms, often combined to leverage both spatial and temporal data characteristics. These efforts are crucial for ensuring the security and reliability of critical infrastructure, with implications for power system stability, efficient energy management, and overall grid resilience.