用对话式AI检测变电站异常，比传统方法更快更准。
Leveraging Conversational Generative AI for Anomaly Detection in Digital Substations
- 用生成式AI构建对话系统，自动分析电力通信异常
- 对未知威胁响应速度比机器学习快，错误率更低
- 适合电力系统安全团队快速应对新型网络攻击
本研究针对数字变电站的网络安全挑战，提出一种面向任务的对话（ToD）系统，用于检测组播消息中的异常，特别是通用面向对象变电站事件（GOOSE）和采样值（SV）数据。借助生成式人工智能（GenAI）技术，该框架在错误率、可扩展性和适应性方面优于传统人工介入（HITL）流程。尤其在面对新型或未知网络威胁时，其效率与部署速度显著优于机器学习方法，同时保持模型复杂度与精度。研究通过硬件在环（HIL）测试平台生成并提取IEC61850通信消息特征，采用先进性能指标对比了所提异常检测与基于HITL的检测框架。该方法为应对不断演变的网络安全威胁、提升电力系统运行可靠性提供了有效解决方案。
原文摘要 · Abstract (English)
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.
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