BoostGuard: Interpretable Misbehavior Detection in Vehicular Communication Networks
Bibliographic record
Abstract
Wireless Communication and Artificial Intelligence are at the heart of driving the evolution in the transportation industry. Cooperative Intelligent Transportation Systems adopt vehicle-to-vehicle (V2V) technology to allow vehicles to exchange real-time information about speed, heading, and location wirelessly with their surrounding vehicles. Such technology has remarkable benefits for improving vehicles’ safety and awareness, albeit imposing many security risks. Despite the evolving efforts to employ authentication mechanisms, there is no guarantee that the exchanged data is trustworthy. Security breaches causing falsified data can aggressively lead to severe safety damages within vehicular networks. This paper proposes, BoostGuard, a novel interpretable framework for detecting falsified data exchanged as part of five different types of position forging attacks against vehicular networks. BoostGuard mainly adopts data science principles and leverages advanced machine learning techniques (i.e., boosting decision tree ensemble) to boost its generalization capabilities for precisely detecting and classifying attack types. Extensive experiments are conducted over an open-source dataset, reflecting dynamic real-world vehicular environments. The evaluation results demonstrate that our solution outperforms existing solutions with high detection effectiveness and computational time efficiency.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".