A Supervised Model to Detect Suspicious Activities in the Bitcoin Network

Shortly 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 Bitc...

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Main Authors: Al-Hashedi K.G., Magalingam P., Maarop N., Samy G.N., Rahim F.B.A., Shanmugam M., Hasan M.K.
Other Authors: 57224367919
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2024
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spelling my.uniten.dspace-346542024-10-14T11:21:28Z A Supervised Model to Detect Suspicious Activities in the Bitcoin Network Al-Hashedi K.G. Magalingam P. Maarop N. Samy G.N. Rahim F.B.A. Shanmugam M. Hasan M.K. 57224367919 35302809600 45661569600 35303350500 57350579500 36195134500 55057479600 Bitcoin Cybercrime Fraud detection Illicit addresses Machine learning Balancing Bitcoin Classification (of information) Crime Criminal activities Cyber-crimes Fraud detection Fraud detection system Illicit address Labeled dataset Machine-learning Supervised classifiers Suspicious behaviours Verification process Machine learning Shortly 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. Final 2024-10-14T03:21:27Z 2024-10-14T03:21:27Z 2023 Conference Paper 10.1007/978-3-031-25274-7_53 2-s2.0-85150985859 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150985859&doi=10.1007%2f978-3-031-25274-7_53&partnerID=40&md5=85e657ad90c998517bf16868a0f8d971 https://irepository.uniten.edu.my/handle/123456789/34654 584 LNNS 606 615 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Bitcoin
Cybercrime
Fraud detection
Illicit addresses
Machine learning
Balancing
Bitcoin
Classification (of information)
Crime
Criminal activities
Cyber-crimes
Fraud detection
Fraud detection system
Illicit address
Labeled dataset
Machine-learning
Supervised classifiers
Suspicious behaviours
Verification process
Machine learning
spellingShingle Bitcoin
Cybercrime
Fraud detection
Illicit addresses
Machine learning
Balancing
Bitcoin
Classification (of information)
Crime
Criminal activities
Cyber-crimes
Fraud detection
Fraud detection system
Illicit address
Labeled dataset
Machine-learning
Supervised classifiers
Suspicious behaviours
Verification process
Machine learning
Al-Hashedi K.G.
Magalingam P.
Maarop N.
Samy G.N.
Rahim F.B.A.
Shanmugam M.
Hasan M.K.
A Supervised Model to Detect Suspicious Activities in the Bitcoin Network
description Shortly 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.
author2 57224367919
author_facet 57224367919
Al-Hashedi K.G.
Magalingam P.
Maarop N.
Samy G.N.
Rahim F.B.A.
Shanmugam M.
Hasan M.K.
format Conference Paper
author Al-Hashedi K.G.
Magalingam P.
Maarop N.
Samy G.N.
Rahim F.B.A.
Shanmugam M.
Hasan M.K.
author_sort Al-Hashedi K.G.
title A Supervised Model to Detect Suspicious Activities in the Bitcoin Network
title_short A Supervised Model to Detect Suspicious Activities in the Bitcoin Network
title_full A Supervised Model to Detect Suspicious Activities in the Bitcoin Network
title_fullStr A Supervised Model to Detect Suspicious Activities in the Bitcoin Network
title_full_unstemmed A Supervised Model to Detect Suspicious Activities in the Bitcoin Network
title_sort supervised model to detect suspicious activities in the bitcoin network
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2024
_version_ 1814061131458674688
score 13.222552