On the Impact of Data Integrity Attacks on Vehicle-to-Microgrid Services
Bibliographic record
Abstract
With the increasing demands on the power grid, more Electric Vehicles (EVs) will be used as mobile storage units to trade energy and avoid power shortages. The integration of EVs and smart grids has expanded the attack surface and paved the way for adversaries to perform novel and intelligent attacks on the system. Therefore, data integrity attacks in modern smart grids are expected to increase in Vehicle-to-Grid (V2G) and Vehicle-to-Microgrid (V2M) applications. In this paper, we propose a novel scheme to model data integrity attacks in V2M applications. By leveraging unsupervised machine learning, we implement an intelligent detector to encounter the data integrity attacks. Although some of the data integrity attacks are able to deceive the detector, they fail to impact the V2M service operation. Through simulations, we show that performing the data integrity attacks against an increasing number of EVs (i.e. backup energy suppliers) results in reducing the attacks' impact by up to 76.5 %. In addition, doubling the original contribution of EVs alleviates the impact of the data integrity attacks by 60%. On the contrary, doubling the number of microgrids (i.e. demand) raises the attacks' impact by at least 75%.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".