Enhancing security in the ISO 15118–20 EV charging system
Bibliographic record
Abstract
Electric Vehicle (EV) ‘DC Fast Charging’ systems directly connect an EV’s battery to an external charger. A compromised EV charger may damage the EV or be used as part of a demand-side power grid attack. We show that the newest charging standard ISO 15118-20 is not sufficient to prevent charging attacks, as it provides no mechanism to verify charger integrity. We present system and threat models for the attack, before defining an extension to ISO 15118-20 that adds support for firmware integrity verification through remote attestation, while remaining interoperable with non-supporting devices. A proof of concept implementation demonstrates the security improvement by protecting against the specified attack while requiring only 85 bytes of secure storage, 8kB of working memory, and adding less than 0.5 seconds to the length of a charging session. Backwards compatibility with an implementation of the original standard is also demonstrated. • A system and threat model is developed for ISO 15118-20 EV charging • ISO 15118-20 is shown to be insufficient to protect against existing attacks • We develop a remote attestation protocol within ISO 15118-20 to improve security • An experimental platform tests interoperability and evaluates performance overheads
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".