A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection

The smart grid is a revolutionary, intelligent, next-generation power system. Due to its cyber infrastructure nature, it must be able to accurately and detect potential cyber-attacks and take appropriate actions in a timely manner. This paper creates a new intrusion detection model, which is able to classify the binary-class, triple-class, and multi-class cyber-attacks and power-system incidents. The intrusion detection model is based on a whale optimization algorithm (WOA)-trained artificial neural network (ANN). The WOA is applied to initialize and adjust the weight vector of the ANN to achieve the minimum mean square error. The proposed WOA-ANN model can address the challenges of attacks, failure prediction, and failure detection in a power system. We utilize the Mississippi State University and Oak Ridge National Laboratory databases of power-system attacks to demonstrate the proposed model and show the experimental results. The WOA is able to train the ANN to find the optimal weights. We compare the proposed model with other commonly used classifiers. The comparison results show the superiority of the proposed WOA-ANN model.