Modified Red Fox Optimizer with Deep Learning enabled False Data Injection Attack Detection

Recently, power systems are drastically developed and shifted towards cyber-physical (CPPS). The CPPS involve numerous sensor devices which generates enormous quantities of information. data gathered from each sensing component needs to accomplish reliability highly prone attacks. Amongst various kinds attacks, false injection attack (FDIA) can seriously affects energy efficiency CPPS. Current driven approach utilized for designing FDIA frequently depends on distinct environmental assumption conditions making them unrealistic ineffective. In this paper, we present a modified Red Fox Optimizer with Deep Learning enabled detection (MRFODL-FDIAD) in the environment. presented MRFODL-FDIAD technique mainly detects classifies FDIAs It encompasses three stage process namely pre-processing, detection, parameter tuning. For uses multihead attention-based long short term memory (MBALSTM) technique. To improve performance MBALSTM model, MRFO be exploited study. experimental evaluation was performed standard IEEE bus system. Extensive set experimentations highlighted supremacy