Decision support protocol for intrusion detection in VANETs
Bibliographic record
Abstract
Vehicular Ad hoc Networks (VANETs) are so difficult to secure due to the wireless technology and its several known security holes. To protect against attacks, methods and techniques have been developed. The Intrusion Detection System (IDS) can detect malicious actions made to the system. In vehicular ad hoc networks, IDSs are in charge of analyzing incoming and outgoing packets to identify malicious signatures. However, without a decision making mechanism, they are useless. This paper designs a decision making protocol for security information in VANETs. Our study is based on two IDS approaches. In the first one, the IDS are installed on vehicles, while in the second one they are installed on the Road Side Units (RSU). In both approaches, vehicles are grouped according to their speed. Corroboration of an attack is based on a probabilistic model of ratio computation between vehicles or RSUs having answered to the signature of the attack. Our aim is to design a decision support mechanism. The dynamic topology of VANET allows a strong prevention by broadcasting the information. So when an attack occurs, the protocol allows the corroboration of the latter and alert neighboring clusters.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".