Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic Model
Bibliographic record
Abstract
Cybersecurity has increased in importance due to advances in connected vehicle technology. To evaluate the impact of cyberattacks in mixed and fully connected vehicle environments, a novel microscopic traffic model is given that incorporates the connected autonomous vehicle (CAV) penetration rate. The intelligent driver (ID) model assumes uniform driver behavior based on a constant which is unsuitable for this environment. Thus, a variable exponent based on the cyberattack intensity is proposed that integrates the CAV penetration rate. The proposed model is evaluated over a 1000 m circular for 500 s with a platoon of 28 vehicles with 60% of vehicles affected by an attack. The results obtained indicate that cyberattacks reduce traffic stability, particularly at low CAV penetration rates. At high penetration rates, these attacks have less of an impact due to faster reaction time and coordination of unaffected CAVs. Furthermore, the results demonstrate that the proposed model can effectively characterize traffic behavior under cyberattacks, and so can be used to alleviate congestion in the presence of cybersecurity threats.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".