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Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble
Alyaman H Massarani1, Mahmoud M Badr2,3, Mohamed Baza4
1Computer Science and Engineering Department, The American University in Cairo, Cairo 11835, Egypt.
Sensors (Basel, Switzerland)
|July 12, 2025
Summary
This study introduces a novel sensor-driven framework for detecting electricity theft in smart grids. The system uses prototype and meta-level ensemble learning to accurately identify energy theft, even zero-day attacks, with improved efficiency.
Area of Science:
- Electrical Engineering
- Computer Science
- Data Science
Background:
- Electricity theft poses significant economic and stability challenges for smart grids.
- Smart meter sensors are crucial for monitoring grid infrastructure and detecting anomalies.
Purpose of the Study:
- To develop a scalable and accurate sensor-driven framework for electricity theft detection.
- To identify zero-day attacks in smart grid systems.
Main Methods:
- Data compression using Principal Component Analysis (PCA) and K-means clustering to create consumption prototypes.
- Training base-level one-class classifiers (One-Class Support Vector Machine and Gaussian Mixture Model) on these prototypes.
- Fusing classifier outputs using a meta-OCSVM layer for enhanced detection.
Main Results:
- Achieved 92% dataset size reduction while preserving anomaly-relevant features.
- Demonstrated superior performance with 88.45% accuracy and 13.85% false alarm rate on the Irish SMP dataset.
- Reduced training time by over 75%.
Conclusions:
- The proposed framework offers an efficient and accurate solution for real-time electricity theft detection.
- Leveraging smart meter sensor data with advanced machine learning techniques enhances smart grid security and stability.
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