Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/110820
Type of publication: research article
Type of publication (PDB): Straipsnis konferencijos medžiagoje kitose duomenų bazėse / Article in conference proceedings in other databases (P1c)
Field of Science: Matematika / Mathematics (N001)
Author(s): Lašas, Karolis;Kasputytė, Gabrielė;Užupytė, Rūta;Krilavičius, Tomas
Title: Fraudulent behaviour identification in ethereum blockchain
Is part of: CEUR Workshop proceedings [electronic resource]: IVUS 2020, Information society and university studies, Kaunas, Lithuania, 23 April, 2020: proceedings. Aachen : CEUR-WS, 2020, Vol. 2698
Extent: p. 78-85
Date: 2020
Keywords: Ethereum;Cryptocurrency;Blockchain;Fraudulent activity;K-Means clustering;Support vector machine;Random forest classifier
Abstract: The 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 model
Internet: https://www.vdu.lt/cris/bitstream/20.500.12259/110820/2/ISSN1613-0073_2020_V_2698.PG_78-85.pdf
https://hdl.handle.net/20.500.12259/110820
Affiliation(s): Baltijos pažangių technologijų institutas
Baltijos pažangių technologijų institutas, Vilnius
Matematikos ir statistikos katedra
Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:3. Konferencijų medžiaga / Conference materials
Universiteto mokslo publikacijos / University Research Publications

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