MétaCan
Menu
Back to cohort
Record W2736538736

Establishing security and privacy in wave-enabled vehicular ad hoc networks

2013· article· en· W2736538736 on OpenAlexaff
Jelena Mišić, Rasit Eskicioglu, Subir Biswas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceVehicular ad hoc networkAnonymityComputer securityWireless ad hoc networkAuthentication (law)Computer networkMessage authentication codeCommunication sourceCryptographyWirelessTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.176
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicVehicular Ad Hoc Networks (VANETs)French-language works237,207