Anomaly detection in a smart grid using wavelet transform, variance fractal dimension and an artificial neural network
Bibliographic record
Abstract
This paper presents a method for detecting anomalous power consumption patterns attacks, using a discrete wavelet transform, as well as the variance fractal dimension (VFD) and an artificial neural network (ANN) for a smart grid. The main procedure of the proposed algorithm consists of the following steps: (i) Finding normal and anomalous patterns of power consumption to train the proposed method, (ii) Applying wavelet transform to power consumption patterns to extract features, (iii) Applying the VFD to the extracted features from Step 3 as an input, (iv) Training an ANN with the extracted features from Step 3, and (v) Launching the trained ANN from Step 4 to detect the anomalous power consumption attack based on a threshold. The proposed method can detect an anomalous power consumption attack with 51% accuracy in the worst case scenario.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".