Establishing security and privacy in wave-enabled vehicular ad hoc networks
Bibliographic record
Abstract
Security and privacy are among the growing concerns of a Vehicular Ad hoc Network (VANET) which requires a high degree of liability from its participants. In this dissertation, We address security, anonymity and privacy challenges of VANETs in the light of the IEEE standards for vehicular communications. VANET provides a variety of road-safety and other applications through wireless devices installed in vehicles and roadside infrastructure. A roadside infrastructure in VANET is generally public, and is prone to several different malicious attacks including node compromise, impersonation, and false message delivery attacks. Therefore, a user of a VANET must verify the integrity of a message that is delivered from a roadside infrastructure. On the other hand, a vehicle-originated message should be anonymous in order to ensure user-privacy in a VANET. However, a vehicle must not be able to take advantage of its anonymity for any misbehavior like sending false messages or malicious updates to other vehicles or a roadside infrastructure. We use proxy signature, identity-based signature, and elliptic curve cryptosystems to provide authentication for infrastructure generated messages, and anonymous authentication for vehicle originated messages. Authentication in a dense traffic condition is a challenge for a receiving entity as it incurs a processing delay at the receiving end. We address this issue with a dynamic approach that selectively verifies received messages based on a message's MAC-layer priority and a sender's information relevance. This approach makes a trade-off between priority and fairness in vehicular message authentication. We develop a network simulator to measure the impact of our authentication schemes over a WAVE protocol stack. Also, we investigate how some of the MAC-layer weaknesses may impair the security of a VANET. Our solutions are lightweight, bandwidth friendly and compatible to the current standards of vehicular communications.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".