Leveraging Conversational Generative AI for Anomaly Detection in Digital Substations
This addresses cybersecurity challenges for power systems, offering a more efficient solution than existing methods, though it appears incremental as it builds on generative AI for a specific domain.
The study tackled cybersecurity in digital substations by proposing a task-oriented dialogue system for anomaly detection in multicast messages, achieving superior error reduction, scalability, and adaptability compared to traditional human-in-the-loop methods.
This study addresses critical challenges of cybersecurity in digital substations by proposing an innovative task-oriented dialogue (ToD) system for anomaly detection (AD) in multicast messages, specifically, generic object oriented substation event (GOOSE) and sampled value (SV) datasets. Leveraging generative artificial intelligence (GenAI) technology, the proposed framework demonstrates superior error reduction, scalability, and adaptability compared with traditional human-in-the-loop (HITL) processes. Notably, this methodology offers significant advantages over machine learning (ML) techniques in terms of efficiency and implementation speed when confronting novel and/or unknown cyber threats, while also maintaining model complexity and precision. The research employs advanced performance metrics to conduct a comparative assessment between the proposed AD and HITL-based AD frameworks, utilizing a hardware-in-the-loop (HIL) testbed for generating and extracting features of IEC61850 communication messages. This approach presents a promising solution for enhancing the reliability of power system operations in the face of evolving cybersecurity challenges.