Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/88928
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Cunha, Leandro L. | por |
dc.contributor.author | Brito, Miguel A. | por |
dc.date.accessioned | 2024-02-21T16:11:07Z | - |
dc.date.issued | 2023 | - |
dc.identifier.isbn | 9789898704504 | por |
dc.identifier.uri | https://hdl.handle.net/1822/88928 | - |
dc.description.abstract | Cryptocurrencies have gained tremendous popularity in recent years, with the rise of Bitcoin and other altcoins. However, this surge in popularity has also attracted fraudulent activities, such as scams, phishing, and money laundering. Particularly, machine learning (ML) algorithms have the potential to detect these fraudulent patterns. However, since in the fraud detection (FD) domain labels are scarce and most times very hard to get, traditional supervised ML models cannot be applied. Additionally, traditional unsupervised anomaly detection (AD) algorithms, generally, lead to high false positive rates. Therefore, this study is intended to explore the feasibility of using AD and active learning (AL) algorithms to uncover new fraudulent patterns in cryptocurrency transactions, assuming minimal access to labels. | por |
dc.description.sponsorship | FCT - Fundação para a Ciência e a Tecnologia (UIDB/00319/2020) | por |
dc.language.iso | eng | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT | por |
dc.rights | restrictedAccess | por |
dc.subject | Active Learning | por |
dc.subject | Anomaly Detection | por |
dc.subject | Cryptocurrencies | por |
dc.subject | Fraud Detection | por |
dc.subject | Machine Learning | por |
dc.subject | Unsupervised Learning | por |
dc.title | Anomaly detection in cryptocurrency transactions with active learning | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
oaire.citationStartPage | 359 | por |
oaire.citationEndPage | 363 | por |
dc.date.updated | 2024-02-07T22:16:16Z | - |
dc.date.embargo | 10000-01-01 | - |
dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
sdum.export.identifier | 13181 | - |
sdum.conferencePublication | Proceedings of the International Conferences on ICT, Society, and Human Beings 2023, ICT 2023; and e-Health 2023, EH 2023; Connected Smart Cities 2023, CSC 2023; and Big Data Analytics, Data Mining and Computational Intelligence 2023, BigDaCI 2023 | por |
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Ficheiro | Descrição | Tamanho | Formato | |
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Anomaly Detection in Cryptocurrency transactions with active learning.pdf Acesso restrito! | 140,16 kB | Adobe PDF | Ver/Abrir |