Application of Generative Artificial Intelligence in Minimizing Cyber Attacks on Vehicular Networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper explores the innovative applications of Generative Artificial Intelligence (GenAI) for strengthening the cybersecurity of vehicular networks. With the advent of intelligent transport systems and autonomous vehicles, the cybersecurity landscape has evolved significantly, which necessitating new strategies to tackle sophisticated threats. GenAI provides advanced capabilities for automating defenses, enhancing threat intelligence, and fostering dynamic security frameworks in vehicular networks. However, the incorporation of GenAI also introduces new risks, requiring robust ethical, legal, and technical oversight. This research paper outlines the current state of GenAI in vehicular network cybersecurity, showcases the Vehicular Threat Intelligence Flowchart (VTIF), focuses on the threat detection rule algorithm in VTIF, highlights the potential benefits and challenges, and proposes future research directions for developing resilient and ethical cybersecurity mechanisms.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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