Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/110820
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dc.contributor.authorLašas, Karolis-
dc.contributor.authorKasputytė, Gabrielė-
dc.contributor.authorUžupytė, Rūta-
dc.contributor.authorKrilavičius, Tomas-
dc.coverage.spatialDE-
dc.date.accessioned2020-10-27T19:03:44Z-
dc.date.available2020-10-27T19:03:44Z-
dc.date.issued2020-
dc.identifier.issn16130073-
dc.identifier.otherVDU02-000064549-
dc.identifier.urihttps://www.vdu.lt/cris/bitstream/20.500.12259/110820/2/ISSN1613-0073_2020_V_2698.PG_78-85.pdf-
dc.identifier.urihttps://hdl.handle.net/20.500.12259/110820-
dc.description.abstractThe phenomenon of cryptocurrencies continues to draw a lot of attention from investors, innovators and the general public. There are over 1300 different cryptocurrencies, including Bitcoin, Ethereum and Litecoin. While the scope of blockchain technology and cryptocurrencies continues to increase, identification of unethical and fraudulent behaviour still remains an open issue. The absence of regulation of the cryptocurrencies ecosystem and the lack of transparency of the transactions may lead to an increased number of fraudulent cases. In this research, we have analyzed the possibility to identify fraudulent behaviour using different classification techniques. Based on Etherium transactional data, we constructed a transaction network which was analyzed using a graph traversal algorithm. Data clustering was performed using three machine learning algorithms: k-means clustering, Support Vector Machine and random forest classifier. The performance of the classifiers was evaluated using a few accuracy metrics that can be calculated from confusion matrix. Research results revealed that the best performance was achieved using a random forest classification modelen
dc.description.sponsorshipBaltijos pažangių technologijų institutas-
dc.description.sponsorshipBaltijos pažangių technologijų institutas, Vilnius-
dc.description.sponsorshipMatematikos ir statistikos katedra-
dc.description.sponsorshipTaikomosios informatikos katedra-
dc.description.sponsorshipVytauto Didžiojo universitetas-
dc.format.extentp. 78-85-
dc.language.isoen-
dc.relation.ispartofCEUR Workshop proceedings [electronic resource]: IVUS 2020, Information society and university studies, Kaunas, Lithuania, 23 April, 2020: proceedings. Aachen : CEUR-WS, 2020, Vol. 2698-
dc.relation.isreferencedbyScopus-
dc.rightshttps://creativecommons.org/licenses/by/4.0/-
dc.subjectEthereumen
dc.subjectCryptocurrencyen
dc.subjectBlockchainen
dc.subjectFraudulent activityen
dc.subjectK-Means clusteringen
dc.subjectSupport vector machineen
dc.subjectRandom forest classifieren
dc.subject.classificationStraipsnis konferencijos medžiagoje kitose duomenų bazėse / Article in conference proceedings in other databases (P1c)-
dc.subject.otherMatematika / Mathematics (N001)-
dc.titleFraudulent behaviour identification in ethereum blockchainen
dc.typeresearch article-
dcterms.bibliographicCitation18-
dc.date.updated2020-10-30T10:17Z-
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item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptMatematikos ir statistikos katedra-
crisitem.author.deptMatematikos ir statistikos katedra-
crisitem.author.deptInformatikos fakultetas-
crisitem.author.deptTaikomosios informatikos katedra-
Appears in Collections:3. Konferencijų medžiaga / Conference materials
Universiteto mokslo publikacijos / University Research Publications
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