Ensuring a Resilient and Secure EV Charging Infrastructure for Sustainable Transportation
Bibliographic record
Abstract
The increased greenhouse gas emissions and their threat on the environment are fueling society's embrace of a green mindset. As part of their fight against climate change, governments are diligently working on shifting the traditional transportation system to a greener one, mainly driven by Electric Vehicles (EVs). EVs have become a major component of the global push to combat climate change, owing to their ability to reduce the emissions of the transportation sector especially when coupled with renewable energy resources. To support the exponential rise in EV numbers, EV Charging Stations (EVCSs) are being deployed rapidly by operators and manufacturers alike. As a result, EVCSs have become an indispensable element of the transportation system. This highlights the need for a reliable and secure ecosystem to support the charging needs of EVs and achieve a sustainable transportation sector. Our research group studies the EVCS ecosystem security through examining its different components. The importance of the security of this ecosystem originates from the crucial service it provides and its connection to critical infrastructure such as the power grid. Our work focuses on uncovering the vulnerabilities of the EV ecosystem that can allow attackers to destabilize the power grid as well as developing to secure this ecosystem and Machine/Deep Learning intrusion/attack detection.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".