Performance Analysis and Comparison of Anomaly-based Intrusion Detection in Vehicular Ad hoc Networks

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dc.contributor.authorShams, Erfan A.
dc.contributor.authorUlusoy, Ali Hakan
dc.contributor.authorRizaner, Ahmet
dc.coverage.issue4cs
dc.coverage.volume29cs
dc.date.accessioned2021-04-30T12:26:29Z
dc.date.available2021-04-30T12:26:29Z
dc.date.issued2020-12cs
dc.description.abstractSecurity and safety applications of Vehicular Ad hoc Networks (VANETs) are developed to improve the traffic flow. While safety applications in VANETs provide warnings and information for the vehicle and other units in the area, malicious behaviors can render this very purpose meaningless. Intrusion Detection Systems (IDSs) are key features for identifying the presence of faulty or malicious behaviors. Support Vector Machine (SVM) is an efficient tool for anomaly detection and it can be employed for intrusion detection based on the metrics of a known attack or normal behavior. Dropping and or delaying network packets are two of the most common variants among other methods in Denial of Service (DoS) attacks. Hence an IDS which can detect both variants can detect similar types of DoS attacks. The result of the study is obtained by designing and implementing an SVM detection module into computer-generated simulation, which depicts a successful outcome in detection of mentioned DoS attack variants.en
dc.formattextcs
dc.format.extent664-671cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2020 vol. 29, č. 4, s. 664-671. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2020.0664en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/196621
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2020/20_04_0664_0671.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectVehicular ad hoc networksen
dc.subjectsupport vector machinesen
dc.subjectdenial of service attacken
dc.subjectintrusion detectionen
dc.subjectmachine learningen
dc.titlePerformance Analysis and Comparison of Anomaly-based Intrusion Detection in Vehicular Ad hoc Networksen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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