Impact of the Open Charge Point Protocol Between the Electric Vehicle and the Fast Charging Station on the Cybersecurity of the Smart Grid
Bibliographic record
Abstract
Cyber-physical security is becoming an important issue within the smart grid. The growth in sales of the electric vehicles (EVs) dictates the need for high-power fast charging stations (HP-FCS). The operation and control of such HP-FCS requires the integration of information and communication technologies (ICT), which makes such critical infrastructure prone to cyber-attacks and hence compromising the charging of the EVs in particular when operating in vehicle-to-grid mode. In Canada, the Open Charge Point Protocol (OCPP) is considered one of the most popular protocols used in HP-FCS. This charging protocol does not include built-in security measures against cyberattacks and hence it may represent an access point to the unauthorized users. In this paper, potential threats against HP-FCS that uses the OCPP are demonstrated through the cyberattacks applied to microgrid equipped with distributed renewable energy resources. Furthermore, the paper demonstrates how to implement a cyber-attack on such a microgrid when operating in an islanded mode while investigating the impact of such cyberphysical attacks on its components.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".