A Fast and Effective Automated Wavelet-Deep learning-based Method to Detect Cyberattacks in Microgrids with EV Fast Charging Stations
Bibliographic record
Abstract
The electric vehicles within the microgrid operate in a vehicle-to-grid (V2G) mode to provide ancillary services to support the operation of renewable energy resources (RERs). However, the threat of cyberattacks on electric vehicle fast charging stations (EVFCSs) is a pressing concern. When these attacks occur, they disable the EVFCSs, rendering them inaccessible and hindering the ancillary service provision, which in turn disrupts the operation of the microgrid. In response to this, this paper presents a novel and effective approach to detect cyberattacks targeting EVFCSs in microgrids embedded with RERs. The proposed method combines wavelets and convolution neural networks (CNNs) to detect such attacks quickly and effectively. The results have shown that the proposed approach detected cyberattacks with a high detection accuracy of 99.39 % and a low computational time of 3.5 seconds.
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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 | 0.001 |
| 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".