An Automated Wavelet Generation Tool for Cyberattack Detection in Substation Automation Systems
Bibliographic record
Abstract
This paper presents a novel method, the Automated Custom Wavelet Generation-Deep Learning-based classifier (ACWGDL), for cyberattack detection in smart grids. The proposed method generates custom wavelets for enhanced feature extraction of cyberattacks on substation automation systems (SASs). Physical and network data from a substation is converted into time-frequency spectrograms using the Discrete Wavelet Transform (DWT) with the custom generated wavelets. Deep learning is implemented with a convolutional neural network to classify the generated spectrograms as cyberattacks from normal cases and power quality disturbance cases. The proposed method has been tested on two publicly available datasets from two different substation automation systems. The results have shown that the use of the custom wavelets was able to enhance the detection accuracy of the cyberattacks by up to 11% compared to the existing benchmark wavelets. The proposed method is versatile and has demonstrated its effectiveness in enhancing the detection accuracy of the cyberattacks irrespective of the choice of the datasets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".