Secure the smart grid by machine learning

Smart Grid Technology has to tighten its security because of the growing threat of cyberattacks. Machine learning approaches are more effective in detecting assaults than conventional methods. The difficulties of detecting hostile activity and infiltration in intelligent grid communication networks were investigated with machine learning techniques. In this work, we apply machine learning methods to determine if the measurements came from an attack or a secure environment. By using several machine learning methods, including Perceptron, Logistic Regression, Support Vector Machine, and the K-Nearest Neighbors algorithm, the suggested system hope to foresee the occurrence of erroneous data injections in the smart grid.