This report provides a comprehensive analysis of a novel intrusion detection model for smart grids, the WOA-ANN model, introduced by Haghnegahdar and Wang (2019). The main area of focus is the cyber infrastructure of smart grids and the need for accurate and timely detection of cyber-attacks. The proposed solution involves training an artificial neural network (ANN) with a whale optimization algorithm (WOA). The WOA is utilized to initialize and adjust the weight vector of the ANN to minimize the mean square error. The WOA-ANN model is designed to classify binary-class, triple-class, and multi-class cyber-attacks and power-system incidents. The model's performance is evaluated using the Mississippi State University and Oak Ridge National Laboratory databases. The results demonstrate the superiority of the WOA-ANN model compared to other classifiers. The conclusions highlight the effectiveness of the WOA-ANN model in addressing challenges related to attacks and failure prediction in power systems. Future work could involve exploring the application of this model to different types of cyber-attacks and comparing it with other advanced machine learning techniques. Haghnegahdar, L., & Wang, Y. (2019). A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection. *Neural Computing and Applications*, *32*(10), 6105-6116.
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