Security Vulnerability Metrics for Connected Vehicles
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
Software integration in modern vehicles is continuously expanding. This is due to the fact that vehicle manufacturers are always trying to enhance and add more innovative and competitive features that may rely on complex software functionalities. However, these features come at a cost. They amplify the security vulnerabilities in vehicles and lead to more security issues in today's automobiles. As a result, the need for identifying vulnerable components in a vehicle software system has become crucial. Security experts need to know which components of the vehicle software system can be exploited for attacks and should focus their testing and inspection efforts on it. Nevertheless, it is a challenging and costly task to identify these weak components in a vehicle's system. In this paper, we propose some security vulnerability metrics for connected vehicles that aim to assist software testers during the development life-cycle in order to identify the frail links that put the vehicle at highsecurity risks. Vulnerable function assessment can give software testers a good idea about which components in a connected vehicle need to be prioritized in order to mitigate the risk and hence secure the vehicle. The proposed metrics were applied to OpenPilot - a software that provides Autopilot feature - and has been integrated with 48 different vehicles.. The application shows how the defined metrics can be effectively used to quantitatively measure the vulnerabilities of a vehicle software system.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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