Non-Intrusive Load Monitoring based Demand Prediction for Smart Meter Attack Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The global implementation of smart meters that measure and communicate residential electricity consumption has resulted in the creation of new energy efficiency services such as automated energy management systems and billing systems. In view of the vulnerability of smart meters to cyber and physical attacks, this research presents a short-term load prediction method that uses energy disaggregation, to detect the False Data Injection (FDI) attack on smart meters. This method is constructed of an edge detection based Non-Intrusive Load Monitoring (NILM) module for energy disaggregation and a load forecaster. In the first step, we attempt to determine when the appliances are switching on/off. Second, the acquired switching events would be utilized as an input for machine learning algorithms including Support Vector Regression (SVR) and Elman Neural Network (ENN) to improve performance of the load forecaster for detecting FDI attacks. Validation of the results based on the data collected from twenty actual UK houses has indicated that the recommended method is a great solution for detecting cyberattacks on residential smart meters.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it