Shared Mobility for Transport and Its Environmental Impact VeSIPreS: A Vehicular Soft Integrity Preservation Scheme for Shared Mobility
Bibliographic record
Abstract
Car manufacturers are noticing and encouraging a trend away from individual mobility, where a vehicle is owned and driven by one or only a few other persons, and towards shared-mobility concepts. That means that many different people use and have access to the same vehicle. An attacker disguised as a regular short-time user can use the additional attack vectors (s)he gets by having physical access to tamper the vehicle’s software. The software takes a continuously more crucial role in cars for autonomous driving, and manipulations can have catastrophic consequences for the persons on board. Currently, there is no mechanism available to the vehicle owner to detect such manipulations in the vehicle done by the attacker (short-time user). In this work, a novel vehicle attestation scheme called Vehicular Soft Integrity Preservation Scheme (VeSIPreS) is proposed to detect tampering in the software stack of a vehicle and guarantee the upcoming driver that the previous user has not changed the software of the vehicle. The solution consists of a software module in the vehicle and a mobile-based user application for the vehicle owner to monitor the vehicle’s soft integrity. Inside the vehicle, the software module is implemented in the central gateway, which acts as the primary security component. VeSIPreS uses Trusted Platform Module (TPM) in the central gateway, which anchors trust in our proposed solution. This paper also provides a proof-of-concept implementation with a TPM, demonstrating its application and deployment feasibility and presentig a security analysis to show the security of VeSIPreS.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".