AI-Driven Threat Intelligence for Proactive Cybersecurity in Smart Grid Systems
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P103Keywords:
Smart Grid Security, Cyber Threat Intelligence, AI-Driven Cybersecurity, Anomaly Detection, Pattern Recognition, Predictive Analytics, Machine Learning, Deep Learning, Threat Detection, Intrusion ResponseAbstract
Smart grid systems, which integrate advanced information and communication technologies (ICT) with traditional power grid infrastructure, offer significant benefits in terms of efficiency, reliability, and sustainability. However, these systems are also increasingly vulnerable to cyber threats due to their complex and interconnected nature. Traditional cybersecurity measures are often reactive and struggle to keep pace with the evolving threat landscape. This paper explores the application of artificial intelligence (AI) in driving threat intelligence for proactive cybersecurity in smart grid systems. We discuss the challenges and opportunities presented by AI in this context, present a framework for AI-driven threat intelligence, and evaluate its effectiveness through case studies and simulations. The paper also includes a detailed algorithm for threat detection and response, and provides recommendations for future research and implementation
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