EV Charging Infrastructure Discovery to Contextualize Its Deployment Security
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electric Vehicle Charging Stations (EVCSs) have been shown to be susceptible to remote exploitation due to manufacturer-induced vulnerabilities, demonstrated by recent attacks on this ecosystem. What is more alarming is that compromising these high-wattage IoT systems can be leveraged to perform coordinated oscillatory load attacks against the power grid which could lead to the instability of this critical infrastructure. In this paper, we investigate a previously sidelined aspect of EVCS security. We analyze the deployment security of EVCSs and highlight operator-induced vulnerabilities rendering the ecosystem exposed to remote intrusions. We create an advanced discovery technique that leverages Web interface artifacts to dynamically discover new charging station vendors. As a result, we uncover 33,320 charging station management systems in the wild. Consequently, we study the deployment security of the charging stations and identify that 28,046 EVCSs were found to be vulnerable to eavesdropping, and around 24% of the studied EVCSs are deployed with default configurations exposing the ecosystem to a Mirai-like attack vector. Aligned with this finding, we discover that the EVCS ecosystem has been targeted by nefarious IoT malware such as Mirai and its variants. This demonstrates that further security measures should be implemented by vendors and operators to ensure the security of this vital ecosystem. Consequently, we provide a comprehensive recommendation for securing the deployment of EVCSs.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it