Complementing public key infrastructure to secure vehicular ad hoc networks [Security and Privacy in Emerging Wireless Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vehicular ad hoc networks are emerging as an effective technology for providing a wide range of safety applications to by-vehicle passengers. Ensuring secure operation is one of the prerequisites for deploying reliable VANETs. In this article we argue that public key infrastructure is the most viable mechanism for securing VANETs as it can meet most VANET security requirements. However, PKI cannot provide certain security requirements such as location privacy, efficient authentication, and distributed and fair revocation. To complement the security services provided by PKI, we introduce complementary security mechanisms that can meet the aforementioned security requirements. Since denial of service attacks have severe consequences on network availability, which is one of the VANET security requirements, we propose a mechanism for mitigating the effect of DoS attacks in VANETs. Simulation results show that the complementary mechanisms together with PKI can efficiently secure VANETs.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 it