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Metadados | Descrição | Idioma |
---|---|---|
Autor(es): dc.contributor | Univ Ottawa | - |
Autor(es): dc.contributor | Universidade Estadual Paulista (Unesp) | - |
Autor(es): dc.creator | Ruj, Sushmita | - |
Autor(es): dc.creator | Cavenaghi, Marcos A. [UNESP] | - |
Autor(es): dc.creator | Huang, Zhen | - |
Autor(es): dc.creator | Nayak, Amiya | - |
Autor(es): dc.creator | Stojmenovic, Ivan | - |
Autor(es): dc.creator | IEEE | - |
Data de aceite: dc.date.accessioned | 2022-02-22T00:03:41Z | - |
Data de disponibilização: dc.date.available | 2022-02-22T00:03:41Z | - |
Data de envio: dc.date.issued | 2020-12-09 | - |
Data de envio: dc.date.issued | 2020-12-09 | - |
Data de envio: dc.date.issued | 2011-01-01 | - |
Fonte completa do material: dc.identifier | http://hdl.handle.net/11449/194731 | - |
Fonte: dc.identifier.uri | http://educapes.capes.gov.br/handle/11449/194731 | - |
Descrição: dc.description | Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is a very important problem with wide range of implications, including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. It is therefore more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alerts with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. After misbehavior is detected, we do not revoke all the secret credentials of misbehaving nodes, as done in most schemes. Instead, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes. | - |
Descrição: dc.description | NSERC | - |
Descrição: dc.description | Univ Ottawa, SITE, Ottawa, ON K1N 6N5, Canada | - |
Descrição: dc.description | Sao Paulo State Univ, Unesp, BR-05508 Sao Paulo, Brazil | - |
Descrição: dc.description | Sao Paulo State Univ, Unesp, BR-05508 Sao Paulo, Brazil | - |
Descrição: dc.description | NSERC: CRDPJ386874-09 | - |
Formato: dc.format | 5 | - |
Idioma: dc.language | en | - |
Publicador: dc.publisher | Ieee | - |
Relação: dc.relation | 2011 Ieee Vehicular Technology Conference (vtc Fall) | - |
???dc.source???: dc.source | Web of Science | - |
Palavras-chave: dc.subject | Misbehavior detection | - |
Palavras-chave: dc.subject | Location privacy | - |
Palavras-chave: dc.subject | Selfish behavior | - |
Título: dc.title | On Data-centric Misbehavior Detection in VANETs | - |
Aparece nas coleções: | Repositório Institucional - Unesp |
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