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AuthorVan der Heijden, Rens Wouterdc.contributor.author
AuthorLukaseder, Thomasdc.contributor.author
AuthorKargl, Frankdc.contributor.author
Date of accession2018-05-09T07:44:06Zdc.date.accessioned
Available in OPARU since2018-05-09T07:44:06Zdc.date.available
Date of first publication2018-05-09dc.date.issued
AbstractVehicular networks are networks of communicating vehicles, a major enabling technology for future cooperative and autonomous driving technologies. The most important messages in these networks are broadcast-authenticated periodic one-hop beacons, used for safety and traffic efficiency applications such as collision avoidance and traffic jam detection. However, broadcast authenticity is not sufficient to guarantee message correctness. The goal of misbehavior detection is to analyze application data and knowledge about physical processes in these cyber-physical systems to detect incorrect messages, enabling local revocation of vehicles transmitting malicious messages. Comparative studies between detection mechanisms are rare due to the lack of a reference dataset. We take the first steps to address this challenge by introducing the Vehicular Reference Misbehavior Dataset (VeReMi) and a discussion of valid metrics for such an assessment. VeReMi is the first public extensible dataset, allowing anyone to reproduce the generation process, as well as contribute attacks and use the data to compare new detection mechanisms against existing ones. The result of our analysis shows that the acceptance range threshold and the simple speed check are complementary mechanisms that detect different attacks. This supports the intuitive notion that fusion can lead to better results with data, and we suggest that future work should focus on effective fusion with VeReMi as an evaluation baseline.dc.description.abstract
Languageen_USdc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordIntrusion detectiondc.subject
KeywordSecuritydc.subject
KeywordMisbehavior detectiondc.subject
KeywordVehicular networksdc.subject
Dewey Decimal GroupDDC 000 / Computer science, information & general worksdc.subject.ddc
LCSHIntrusion detection systems (Computer security)dc.subject.lcsh
LCSHTraffic safetydc.subject.lcsh
LCSHAutonomous vehiclesdc.subject.lcsh
LCSHVehicular ad hoc networks (Computer networksdc.subject.lcsh
TitleVeReMi: a dataset for comparable evaluation of misbehavior detection in VANETsdc.title
Resource typeBeitrag zu einer Konferenzdc.type
VersionacceptedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-6486dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-6543-0dc.identifier.urn
GNDAbweichendes Verhaltendc.subject.gnd
GNDAutonomes Fahrzeugdc.subject.gnd
GNDFehlererkennungdc.subject.gnd
GNDVANETdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Verteilte Systemeuulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
Conference nameSecureComm 2018 - 14th EAI International Conference on Security and Privacy in Communication Networksuulm.conferenceName
Conference placeSingaporeuulm.conferencePlace
Conference start date2018-08-08uulm.conferenceStartDate
Conference end date2018-08-10uulm.conferenceEndDate
Bibliographyuulmuulm.bibliographie


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