Machine Learning-Based Ransomware Classification of Bitcoin Transactions
Ransomware attacks are one of the most dangerous related crimes in the coin market. To increase the challenge of fighting the attack, early detection of ransomware seems necessary. In this article, we propose a high-performance Bitcoin transaction predictive system that investigates Bitcoin payment...
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2023/6274260 |
| _version_ | 1811171048059568128 |
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| author | Suleiman Ali Alsaif |
| author_facet | Suleiman Ali Alsaif |
| author_sort | Suleiman Ali Alsaif |
| collection | DOAJ |
| description | Ransomware attacks are one of the most dangerous related crimes in the coin market. To increase the challenge of fighting the attack, early detection of ransomware seems necessary. In this article, we propose a high-performance Bitcoin transaction predictive system that investigates Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks into malicious or benign transactions. The proposed approach makes use of three supervised machine learning methods to learn the distinctive patterns in Bitcoin payment transactions, namely, logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost). We evaluate these ML-based predictive models on the BitcoinHeist ransomware dataset in terms of classification accuracy and other evaluation measures such as confusion matrix, recall, and F1-score. It turned out that the experimental results recorded by the XGBoost model achieved an accuracy of 99.08%. As a result, the resulting model accuracy is higher than many recent state-of-the-art models developed to detect ransomware payments in Bitcoin transactions. |
| first_indexed | 2024-04-10T17:07:35Z |
| format | Article |
| id | doaj.art-a6be881935604ca69976b22bff367fad |
| institution | Directory Open Access Journal |
| issn | 1687-9732 |
| language | English |
| last_indexed | 2024-04-10T17:07:35Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj.art-a6be881935604ca69976b22bff367fad2023-02-06T01:40:25ZengHindawi LimitedApplied Computational Intelligence and Soft Computing1687-97322023-01-01202310.1155/2023/6274260Machine Learning-Based Ransomware Classification of Bitcoin TransactionsSuleiman Ali Alsaif0Computer DepartmentRansomware attacks are one of the most dangerous related crimes in the coin market. To increase the challenge of fighting the attack, early detection of ransomware seems necessary. In this article, we propose a high-performance Bitcoin transaction predictive system that investigates Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks into malicious or benign transactions. The proposed approach makes use of three supervised machine learning methods to learn the distinctive patterns in Bitcoin payment transactions, namely, logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost). We evaluate these ML-based predictive models on the BitcoinHeist ransomware dataset in terms of classification accuracy and other evaluation measures such as confusion matrix, recall, and F1-score. It turned out that the experimental results recorded by the XGBoost model achieved an accuracy of 99.08%. As a result, the resulting model accuracy is higher than many recent state-of-the-art models developed to detect ransomware payments in Bitcoin transactions.http://dx.doi.org/10.1155/2023/6274260 |
| spellingShingle | Suleiman Ali Alsaif Machine Learning-Based Ransomware Classification of Bitcoin Transactions Applied Computational Intelligence and Soft Computing |
| title | Machine Learning-Based Ransomware Classification of Bitcoin Transactions |
| title_full | Machine Learning-Based Ransomware Classification of Bitcoin Transactions |
| title_fullStr | Machine Learning-Based Ransomware Classification of Bitcoin Transactions |
| title_full_unstemmed | Machine Learning-Based Ransomware Classification of Bitcoin Transactions |
| title_short | Machine Learning-Based Ransomware Classification of Bitcoin Transactions |
| title_sort | machine learning based ransomware classification of bitcoin transactions |
| url | http://dx.doi.org/10.1155/2023/6274260 |
| work_keys_str_mv | AT suleimanalialsaif machinelearningbasedransomwareclassificationofbitcointransactions |