Intrusion Detection in Smart Power Networks Using Inception-V4 Neural Networks Optimized by Modified Polar Fox Optimization Algorithm for Cyber-Physical Threat Mitigation

Threats that are caused by cyber-attacks on intelligent power networks promote the implementation of sophisticated intrusion detection devices, which can effectively detect advanced attacks. In this paper, a new model is introduced that combines the Modified Polar Fox Optimization Algorithm (MPFA) with an Inception-V4 deep neural network to enhance the effectiveness of the threat detection task. The MPFA optimizes inception-V4 hyperparameters and architecture to balance the exploration and exploitation processes of the courtship learning process and fitness-based scaling. The optimized model on the smart grid monitoring power is shown to perform well; it achieves over 99.5% accuracy, precision, recall, and F1-score on the detection of various attacks, including False Data Injection, Denial-of-Service, and Load Redistribution, and has a favorable computational overhead, thus it can be considered a formidable solution to protect critical smart grid infrastructure. The optimized model, evaluated on the Smart Grid Monitoring Power dataset, achieves state-of-the-art performance with an accuracy of 99.63%, a precision of 99.61%, a recall of 99.65%, and an F1-score of 99.63% for the detection of various cyber-physical attacks, including False Data Injection, Denial-of-Service, and Load Redistribution. It also maintains a favorable computational overhead, thus presenting a formidable solution for protecting critical smart grid infrastructure.