On Data-centric Misbehavior Detection in VANETs
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/194731 |
Resumo: | 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. |
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On Data-centric Misbehavior Detection in VANETsMisbehavior detectionLocation privacySelfish behaviorDetecting 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.NSERCUniv Ottawa, SITE, Ottawa, ON K1N 6N5, CanadaSao Paulo State Univ, Unesp, BR-05508 Sao Paulo, BrazilSao Paulo State Univ, Unesp, BR-05508 Sao Paulo, BrazilNSERC: CRDPJ386874-09IeeeUniv OttawaUniversidade Estadual Paulista (Unesp)Ruj, SushmitaCavenaghi, Marcos A. [UNESP]Huang, ZhenNayak, AmiyaStojmenovic, IvanIEEE2020-12-10T16:35:54Z2020-12-10T16:35:54Z2011-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject52011 Ieee Vehicular Technology Conference (vtc Fall). New York: Ieee, 5 p., 2011.1550-2252http://hdl.handle.net/11449/194731WOS:000298891500284Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2011 Ieee Vehicular Technology Conference (vtc Fall)info:eu-repo/semantics/openAccess2021-10-22T20:18:56Zoai:repositorio.unesp.br:11449/194731Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:04:13.239905Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
On Data-centric Misbehavior Detection in VANETs |
title |
On Data-centric Misbehavior Detection in VANETs |
spellingShingle |
On Data-centric Misbehavior Detection in VANETs Ruj, Sushmita Misbehavior detection Location privacy Selfish behavior |
title_short |
On Data-centric Misbehavior Detection in VANETs |
title_full |
On Data-centric Misbehavior Detection in VANETs |
title_fullStr |
On Data-centric Misbehavior Detection in VANETs |
title_full_unstemmed |
On Data-centric Misbehavior Detection in VANETs |
title_sort |
On Data-centric Misbehavior Detection in VANETs |
author |
Ruj, Sushmita |
author_facet |
Ruj, Sushmita Cavenaghi, Marcos A. [UNESP] Huang, Zhen Nayak, Amiya Stojmenovic, Ivan IEEE |
author_role |
author |
author2 |
Cavenaghi, Marcos A. [UNESP] Huang, Zhen Nayak, Amiya Stojmenovic, Ivan IEEE |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Univ Ottawa Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Ruj, Sushmita Cavenaghi, Marcos A. [UNESP] Huang, Zhen Nayak, Amiya Stojmenovic, Ivan IEEE |
dc.subject.por.fl_str_mv |
Misbehavior detection Location privacy Selfish behavior |
topic |
Misbehavior detection Location privacy Selfish behavior |
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. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-01-01 2020-12-10T16:35:54Z 2020-12-10T16:35:54Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2011 Ieee Vehicular Technology Conference (vtc Fall). New York: Ieee, 5 p., 2011. 1550-2252 http://hdl.handle.net/11449/194731 WOS:000298891500284 |
identifier_str_mv |
2011 Ieee Vehicular Technology Conference (vtc Fall). New York: Ieee, 5 p., 2011. 1550-2252 WOS:000298891500284 |
url |
http://hdl.handle.net/11449/194731 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2011 Ieee Vehicular Technology Conference (vtc Fall) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
5 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129389288751104 |