The Wisdom of the Crowd: High-Fidelity Classification of Cyber-Attacks and Faults in Power Systems Using Ensemble and Machine Learning

• URL: http://arxiv.org/abs/2511.06714v1
• Date: Mon, 10 Nov 2025 05:15:37 GMT
• Title: The Wisdom of the Crowd: High-Fidelity Classification of Cyber-Attacks and Faults in Power Systems Using Ensemble and Machine Learning
• Authors: Emad Abukhousa, Syed Sohail Feroz Syed Afroz, Fahad Alsaeed, Abdulaziz Qwbaiban, Saman Zonouz, A. P. Sakis Meliopoulos,
• Abstract summary: This paper presents a high-fidelity evaluation framework for machine learning (ML)-based classification of cyber-attacks and physical faults.<n>Models were trained on labeled time-domain measurements and evaluated in a real-time streaming environment designed for sub-cycle responsiveness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a high-fidelity evaluation framework for machine learning (ML)-based classification of cyber-attacks and physical faults using electromagnetic transient simulations with digital substation emulation at 4.8 kHz. Twelve ML models, including ensemble algorithms and a multi-layer perceptron (MLP), were trained on labeled time-domain measurements and evaluated in a real-time streaming environment designed for sub-cycle responsiveness. The architecture incorporates a cycle-length smoothing filter and confidence threshold to stabilize decisions. Results show that while several models achieved near-perfect offline accuracies (up to 99.9%), only the MLP sustained robust coverage (98-99%) under streaming, whereas ensembles preserved perfect anomaly precision but abstained frequently (10-49% coverage). These findings demonstrate that offline accuracy alone is an unreliable indicator of field readiness and underscore the need for realistic testing and inference pipelines to ensure dependable classification in inverter-based resources (IBR)-rich networks.

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