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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2024
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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 |
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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 |
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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 |
13.211869 |