A GPT-2 transformer trained on synthetic IEC-61850 traffic plus weak pseudo-labels detects four held-out zero-day attack types with 85-95% accuracy without retraining.
Machine Learning Based Cyber System Restoration for IEC 61850 Based Digital Substations
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abstract
Substation Automation Systems (SAS) that adhere to the International Electrotechnical Commission (IEC) 61850 standard have already been widely implemented across various on-site local substations. However, the digitalization of substations, which involves the use of cyber system, inherently increases their vulnerability to cyberattacks. This paper proposes the detection of cyberattacks through an anomaly-based approach utilizing Machine Learning (ML) methods within central control systems of the power system network. Furthermore, when an anomaly is identified, mitigation and restoration strategies employing concurrent Intelligent Electronic Devices (CIEDs) are utilized to ensure robust substation automation system operations. The proposed ML model is trained using Sampled Value (SV) and Generic Object Oriented Substation Event (GOOSE) data from each substation within the entire transmission system. As a result, the trained ML models can classify cyberattacks and normal faults, while the use of CIEDs contributes to cyberattack mitigation, and substation restoration.
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Detecting Zero-Day Attacks in Digital Substations via In-Context Learning
A GPT-2 transformer trained on synthetic IEC-61850 traffic plus weak pseudo-labels detects four held-out zero-day attack types with 85-95% accuracy without retraining.