Abstract
The North American bulk power system is one of the most vital infrastructures in modern society as it accounts for virtually all the electricity supplied to the United States, Canada, and a portion of Baja Notre California, Mexico. Cyberattacks, of all forms, are becoming increasingly prominent within power networks and other infrastructures whereas their resolution can consume a significant deal of time and monetary resources. This can be further worsened if there are subsequent physical attacks in the wake of a cyberattack, as the system downtime leaves the government, military, and other critical infrastructures incredibly vulnerable This research aims to investigate if different patterns of cyberattacks could be identified with speed using simulation and machine learning algorithms. More specifically, we design a simulation model that can help better defend against cyber threats.