AI-augmented cybersecurity for smart grids in the United States

In this study, we look at using AI and ML to strengthen cybersecurity in the United States by resolving known weaknesses and coming up with a dependable and privacy-aware defense plan that follows the rules. Researching with federated learning, LSTM, and CNN in an AI structure, the system was examined using information from SCADA/ICS systems along with real and simulated datasets, complying with the NERC CIP, FERC orders, and cybersecurity guidelines by the U.S. Department of Energy. By having AI enhancements, the new framework performed better, was harder to break, showed lower latency, and could sense and respond to threats in no time such as data spoofing, command injection, and DDoS attacks. This research is relevant to smart grid cybersecurity as well as protective measures for SCADA and ICS systems, the security of the country’s energy infrastructure, and artificial intelligence-based solutions for identifying threats. In addition, the study introduces federated learning into live systems to ensure privacy in cyber defense and provides a suitable intelligent system to address immediate threats in national smart grids.