Impact of Denial-of-Service Cyberattacks on Hydrogen Refueling Stations in An Integrated Transportation and Electric Power System
Bibliographic record
Abstract
Expanding hydrogen stations is vital for green energy and reducing carbon emissions, but their high costs require efficient operation. Integrating these stations with transportation and power systems can help use excess renewable energy effectively, generate profits, and provide grid services. However, this integration involves sharing sensitive data with the Energy Management System (EMS), which can expose the system to cyberattacks, potentially affecting financial stability and slowing down growth. This paper examines the impact of Denial-of-Service (DoS) cyberattacks on the profitability of hydrogen refueling stations (HRSs). First, the effect of DoS on each critical information signal is analyzed. Based on the signals' vulnerability to cyberattacks, a Distributed Denial-of-Service (DDoS) model is developed to target multiple signals simultaneously, amplifying financial losses for the stations. The results show that a DoS attack on a single signal of hydrogen tank State of Charge (SOC), hydrogen station demand, and grid services signals can cause a damage of 4-5%, 9-10.5%, and 29% respectively, while a DDoS attack over local station measurements can reduce revenue up to 17%. In both cases, the profitability of the hydrogen station falls below the acceptable margin.
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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 | 0.001 |
| 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".