A Novel Infrastructure-Based Worm Spreading Countermeasure for Vehicular Networks
Bibliographic record
Abstract
Vehicular ad hoc networks (VANETs), essential components of intelligent transportation systems, are attracting an increasing amount of interest in research and industrial sectors. As multifunctional mobile nodes that integrate transporting, sensing, information processing, and wireless communication capabilities, vehicular nodes are facing remarkable security issues and are more vulnerable to malware attacks than conventional communication nodes. In this paper, we examine the behaviors and security concerns relating to worm spreading in VANETs. We discuss various approaches for worm spreading in VANETs, and propose an infrastructure-based worm containment (IBWC) strategy. The IBWC problem is modeled as a minimum contamination problem by introducing the expected contamination degree. The simplified Greedy method is then proposed to solve the minimum expected contamination degree problem on road networks. Simulation results show that the proposed method outperforms the existing greedy method and the max-flow based method from both complexity and solution quality aspects.
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 | 0.000 | 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".