AI-Driven Anomaly and Intrusion Detection in Energy Systems: Current Trends and Future Direction

The growing digitalization and interconnection of energy infrastructures have improved operational efficiency but also heightened the risk of exposure to cyber threats. Traditional electrical power and energy systems encompass all infrastructure and processes for generating, transmitting, distributing, and consuming electricity. Conversely, the smart grid represents an advanced paradigm, integrating cyber-physical components to optimize efficiency, reliability, and sustainability. However, this paradigm shift renders the energy sector more susceptible to cyber threats and attacks, necessitating proactive identification and mitigation. This survey provides a comprehensive analysis of the current state of anomaly and intrusion detection systems specifically designed for the energy sector. We review recent advancements in detection methodologies, including machine learning, artificial intelligence, and hybrid techniques, highlighting their effectiveness in identifying potential threats.