Detecting faulty and malicious vehicles using rule-based communications data mining
Bibliographic record
Abstract
The reliability of most safety applications that are based on vehicular communications, depends in turn on the reliability of data received by each vehicle from its neighbors. Routine messages exchanged in Vehicular Ad hoc Networks (VANETs) include crucial information for safety applications such as direction, position, etc. A vehicle failure and/or a malicious vehicle transmitting false information may affect the data collection scheme and cause a disturbance for safety applications. In such a scenario, (1) the faulty/malicious vehicle should be detected rapidly and (2) routine messages exchange should be updated in consequence. To be able to detect the faulty/malicious vehicle, we developed a mechanism that collects, at a single vehicle, data regarding each neighbour transmission, and extracts the temporal correlation rules between vehicles implicated in transmissions in the neighbourhood. With the mechanism, called VANETs Association Rules Mining (VARM), a mining process will take place during a-priori constant historical period. The associations rules formulated during the mining process will be used to detect a faulty or malicious vehicle, i.e., a vehicle which is not correlated with vehicles in the neighbourhood following these rules. To react after this kind of anomaly detection, an 1:N technique is used as a protection for reestablishing the accuracy of the data collection process between vehicles communicating in the neighbourhood. Simulation results demonstrate the efficiency of the VARM scheme.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".