LLM-Driven Real-Time Threat Prediction and Response for Internet of Energy (IoE)

The Internet of Energy (IoE) integrates interconnected devices to optimize energy systems, yet its complexity and resource constraints introduce significant cybersecurity challenges. Traditional intrusion detection systems (IDS) use signature-based or statistical approaches do not adapt to the dynamic threat landscape of IoE. This paper proposes a foundational framework leveraging Large Language Models (LLMs) for proactive cybersecurity in IoE, focusing on real-time threat prediction and response. The framework conceptualizes LLMs to analyze heterogeneous data sources, such as network logs, device behaviors, and contextual energy metrics, to detect anomalies and generate adaptive security policies. The framework enables lightweight LLM designs, privacy-preserving mechanisms, and distributed architectures tailored for IoE’s constrained environments. A comprehensive theoretical analysis evaluates the framework’s feasibility, scalability, resilience, and alignment with IoE requirements, addressing threats like denial-of-service (DoS) attacks, data integrity violations, and privacy breaches. This work provides the foundation for securing next-generation energy systems, emphasizing adaptability and efficiency.