Investigating Cyberattacks Against Off-Grid Solar-Powered Electric Vehicle Charging Stations
Bibliographic record
Abstract
The rapid installation of charging stations is imperative to facilitate the transition to decarbonization of the transport sector and cope with the booming sales of electric vehicles (EVs) in recent years. Yet, the installation of charging stations requires capital intensive and time-consuming network reinforcement investments. The installation of off-grid EV charging stations can be considered as a viable solution to address this challenge in locations where the electric grid is not nearby, or the required investments to upgrade the grid is expensive Solar generation, a battery energy storage system (BESS) and an energy management system (EMS) can be incorporated into EV charging stations to realize off-grid solar-powered EV charging stations. The incorporation of solar generation, a BESS and an EMS transforms the EV charging station into a complex cyber-physical system which is prone to various cyberattacks. In this paper, we investigate vulnerability of off-grid solar-powered EV charging stations to cyberattacks. We demonstrate an attacker can compromise the measurements and control commands in an off-grid solar-powered EV charging station to force the charging station out of service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".