Electricity-Theft Detection for Change-and-Transmit Advanced Metering Infrastructure
Bibliographic record
Abstract
The periodic transmission of the customers’ power consumption readings in the advanced metering infrastructure (AMI) is essential for energy management and billing. To collect the readings efficiently, the change and transmit approach is adopted in AMI (CAT AMI) so that the readings are reported only when there is enough change in the consumption. However, CAT AMI suffers from malicious customers who launch electricity-theft cyberattacks by manipulating their readings to illegally reduce their bills. These attacks can cause hefty financial losses and degrade the grid performance because the readings are used for grid management. In this article, the electricity-theft problem in CAT AMI networks is investigated. We first process a real power consumption readings data set to create a benign data set and propose a new set of cyberattacks to create malicious samples. We then develop a deep-learning-based electricity-theft detection solution to identify malicious customers for the CAT AMI network. The proposed detector uses both the customers’ transmission pattern and CAT readings to learn the correlation between them in order to enhance the detector’s ability in identifying electricity thefts. We conduct extensive experiments to evaluate the performance of our electricity-theft detector, and the results indicate that our detector can accurately detect malicious customers and achieve higher detection rate and lower false alarm than the detectors that are trained only on the CAT readings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".