Publication:
A Supervised Model to Detect Suspicious Activities in the Bitcoin Network

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dc.citedby1
dc.contributor.authorAl-Hashedi K.G.en_US
dc.contributor.authorMagalingam P.en_US
dc.contributor.authorMaarop N.en_US
dc.contributor.authorSamy G.N.en_US
dc.contributor.authorRahim F.B.A.en_US
dc.contributor.authorShanmugam M.en_US
dc.contributor.authorHasan M.K.en_US
dc.contributor.authorid57224367919en_US
dc.contributor.authorid35302809600en_US
dc.contributor.authorid45661569600en_US
dc.contributor.authorid35303350500en_US
dc.contributor.authorid57350579500en_US
dc.contributor.authorid36195134500en_US
dc.contributor.authorid55057479600en_US
dc.date.accessioned2024-10-14T03:21:27Z
dc.date.available2024-10-14T03:21:27Z
dc.date.issued2023
dc.description.abstractShortly after its official launch in 2009, Bitcoin has gained rapid popularity worldwide, which in return attracted a variety of people especially malicious attackers, who get the advantage of its pseudo-anonymity to institute un-traceable threats, scams, and criminal activities. Recently, some Bitcoin thefts have been reported costing millions of dollars, causing serious harm and losses to innocent users or companies that lead them to declare bankruptcy. One of the main characteristics of Bitcoin is its anonymity, which makes Bitcoin the preferred choice for criminals to perform illicit activities that pose difficulties for law enforcement and financial authorities to identify suspicious behavior, making the existing fraud detection systems ineffective. In this paper, we propose a model for detecting suspicious activities in the Bitcoin network. We first construct a labeled dataset by collecting a set of illicit transactions from public online Bitcoin forums, as well as datasets from prior research. Next, a verification and filtration process has been performed to verify the gathered illicit transactions with the original dataset and manually marked them as either legal or illegal. Additionally, a new set of features that are based on time-slice was extracted, the skewed dataset was balanced, and three supervised classifiers (LR, NB, and ANN) were used for evaluating the proposed model. Finally, our findings found that the ANN classifier achieved the best performer among others, which attained Precision, Recall, F1 scores, and AUC of 95.2%, 88.7%, 89.8%, and 91.2% respectively. The performance of the supervised classifiers has significantly improved after balancing the training set. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-25274-7_53
dc.identifier.epage615
dc.identifier.scopus2-s2.0-85150985859
dc.identifier.spage606
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85150985859&doi=10.1007%2f978-3-031-25274-7_53&partnerID=40&md5=85e657ad90c998517bf16868a0f8d971
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34654
dc.identifier.volume584 LNNS
dc.pagecount9
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.subjectBitcoin
dc.subjectCybercrime
dc.subjectFraud detection
dc.subjectIllicit addresses
dc.subjectMachine learning
dc.subjectBalancing
dc.subjectBitcoin
dc.subjectClassification (of information)
dc.subjectCrime
dc.subjectCriminal activities
dc.subjectCyber-crimes
dc.subjectFraud detection
dc.subjectFraud detection system
dc.subjectIllicit address
dc.subjectLabeled dataset
dc.subjectMachine-learning
dc.subjectSupervised classifiers
dc.subjectSuspicious behaviours
dc.subjectVerification process
dc.subjectMachine learning
dc.titleA Supervised Model to Detect Suspicious Activities in the Bitcoin Networken_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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