Authors - Katleho Seatlolo, Khutso Lebea Abstract - This paper investigates the potential of Retrieval Augmented Generation (RAG) technology to enhance the effectiveness of Intrusion Detection Systems (IDS) in the energy sector. By leveraging vast amounts of historical data, real-time threat intelligence, and advanced natural language processing techniques, RAG can significantly improve IDS capabilities in detecting and responding to cyber threats. The paper addresses the limitations of traditional IDS, such as their reliance on predefined signatures and vulnerability databases. It explores how RAG can overcome these limitations by analysing network traffic patterns, identifying anomalies, and correlating them with known attack vectors. The paper discusses the potential benefits of RAG in terms of improved threat detection accuracy, reduced false positives, and enhanced response times. Case studies and research findings are presented to support the argument. Challenges and considerations related to data quality, privacy, and ethical implications are also addressed. The conclusion emphasises the importance of RAG technology in safeguarding smart grids from evolving cyber threats and highlights potential future directions for research and development. The paper aims to explore the potential of RAG technology to enhance IDS in smart grids and contribute to Sustainable Development Goal 9: Industry, Innovation, and Infrastructure. It describes the limitations of traditional IDS, the benefits of RAG technology, and the potential applications of RAG in the energy sector.
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