Generative AI in minimizing cyber-attacks: Developing the Vehicular Threat Intelligence Flowchart
Bibliographic record
Abstract
This paper delves into the innovative applications of Generative Artificial Intelligence (GenAI) in enhancing the cybersecurity of vehicular networks, a critical area given the increasing integration of intelligent transport systems and autonomous vehicles. As vehicular networks become more sophisticated, they also become more susceptible to cyber-attacks that can compromise vehicle control systems, endangering public safety and personal privacy. GenAI offers advanced capabilities for automating defences, improving threat intelligence, and creating dynamic security frameworks that can adapt to emerging threats. This research is a comprehensive overview of the current state of GenAI in the context of vehicular network cybersecurity, highlighting the development and implementation of the Vehicular Threat Intelligence Flowchart (VTIF). The VTIF features a threat detection rule algorithm that automates the identification of cyber threats, significantly improving detection accuracy. While the integration of GenAI presents substantial benefits, it also introduces new risks, necessitating robust ethical, legal, and technical oversight. This paper outlines the potential advantages and challenges of employing GenAI in vehicular cybersecurity and proposes future research directions aimed at developing resilient and ethical cybersecurity mechanisms.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".