Detection and Localization of Stealth False Data Injection Attacks in Active Power Distribution Systems Using an Ensemble of Deep CNNs
Bibliographic record
Abstract
Cyber-physical power systems” reliance on cyberspace makes them vulnerable to cyber-attacks, particularly false data injection attacks (FDIAs), where the aim is to alter the state estimation (SE) results by changing meters” readings. Because of distribution systems” properties such as the lower measurement redundancy and varying loads, existing methods cannot be used to accurately detect and localize an FDIA. To fill these gaps and to deal with the rarity of FDIAs in distribution systems, we propose an ensemble of deep convolutional neural networks (CNNs) to detect and localize FDIAs in active balance, and unbalanced distribution systems. To this end, first, a dataset is created using different attacking scenarios and the possible reconfigurations and renewable generation scenarios in the system. The records are in the form of WLS-generated voltage estimation of PQ buses with different balancing ratios between attacked and normal records. Then, these datasets are used to train different CNNs. These CNNs' outputs are merged using a multilayer perceptron network. Finally, FDIA is detected and localized by the proposed ensemble model in a balanced and unbalanced power distribution system. Results of simulations on IEEE 33-bus and the modified IEEE 13-bus networks verify that the ensemble model can distinguish between normal and attacked records with great accuracy according to the area under the curve (AUC) criteria, thus giving the operators a powerful tool to defend against FDIAs.
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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".