On data-centric misbehavior detection in VANETs

dc.contributor.authorRuj, Sushmita
dc.contributor.authorCavenaghi, Marcos Antônio [UNESP]
dc.contributor.authorHuang, Zhen
dc.contributor.authorNayak, Amiya
dc.contributor.authorStojmenovic, Ivan
dc.contributor.institutionUniversity of Ottawa
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:26:20Z
dc.date.available2014-05-27T11:26:20Z
dc.date.issued2011-12-23
dc.description.abstractDetecting 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. © 2011 IEEE.en
dc.description.affiliationSITE University of Ottawa
dc.description.affiliationUnesp Sao Paulo State University DCo
dc.description.affiliationUnespUnesp Sao Paulo State University DCo
dc.identifierhttp://dx.doi.org/10.1109/VETECF.2011.6093096
dc.identifier.citationIEEE Vehicular Technology Conference.
dc.identifier.doi10.1109/VETECF.2011.6093096
dc.identifier.issn1550-2252
dc.identifier.lattes8163849451440263
dc.identifier.scopus2-s2.0-83755171614
dc.identifier.urihttp://hdl.handle.net/11449/73081
dc.language.isoeng
dc.relation.ispartofIEEE Vehicular Technology Conference
dc.relation.ispartofsjr0,226
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectLocation privacy
dc.subjectMisbehavior detection
dc.subjectSelfish behavior
dc.subjectCertification authorities
dc.subjectCommunication cost
dc.subjectCongestion avoidance
dc.subjectData centric
dc.subjectGaining access
dc.subjectMalicious nodes
dc.subjectMisbehaving nodes
dc.subjectSybil attack
dc.subjectVehicle position
dc.subjectVehicular ad hoc networks
dc.subjectAd hoc networks
dc.subjectComputer crime
dc.subjectMobile ad hoc networks
dc.titleOn data-centric misbehavior detection in VANETsen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
unesp.author.lattes8163849451440263
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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